%matplotlib inline
from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, RandomizedSearchCV, GridSearchCV
from sklearn.preprocessing import PowerTransformer, QuantileTransformer, StandardScaler
from sklearn.decomposition import PCA
from sklearn.datasets import make_regression
from sklearn.pipeline import Pipeline
from IPython.display import display
import ipyparallel as ipp
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import itertools
import MESS
from MESS.util import set_params
## Set some pandas options to show the full rows and columns of a DF
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)
pd.set_option('display.width', 1000)
## Where do you want the simulation output to life?
analysis_dir = "/home/isaac/Continuosity/MESS/analysis/"
analysis_dir = analysis_dir + "/parameter-estimation-cv/"
if not os.path.exists(analysis_dir):
os.mkdir(analysis_dir)
## This is a toy example that uses simulated correlated features
X, y = make_regression(n_samples=100, n_targets=2, n_features=4, n_informative=2,
random_state=0, shuffle=False, noise=10)
regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=100)
regr.fit(X, y)
print(regr.feature_importances_)
print(regr.predict([[0, 0, 0, 0]]))
plt.scatter(y[:,0], y[:,1])
[0.00446853 0.99553147 0. 0. ] [[-14.56605054 -15.80068559]]
<matplotlib.collections.PathCollection at 0x7f8437195250>
r = MESS.Region("r1")
r.paramsdict["generations"] = 0
r.set_param("project_dir", analysis_dir)
r.set_param("m", (0.001, 0.01))
r.set_param("J", (500, 10000))
r.islands["Loc1"].paramsdict
#r.run(sims=1)
Project directory exists. Additional simulations will be appended.
OrderedDict([('name', 'Loc1'), ('J', 3806), ('m', 0.0030560719034298514), ('speciation_rate', 0), ('background_death', 0.25)])
If you haven't already done so you should fire up an ipcluster instance on your HPC/workstation. SSH to the computer that is running this notebook and do this:
ipcluster start -n 40 --cluster-id="MESS-Rich" --daemonize
Later you can stop this ipcluster with this command:
ipcluster stop --cluster-id="MESS-Rich"
Now create an client interface to the cluster (which we'll call ipyclient). You can see how many nodes we have access to by asking for the length if the ipyclient object.
ipyclient = ipp.Client(cluster_id="MESS-Rich")
print(len(ipyclient))
40
r = MESS.Region("r1")
r._log_files = True
r.set_param("generations", 0.5)
r.set_param("m", (0.001, 0.05))
r.set_param("speciation_rate", (0.0001, 0.001))
r.set_param("project_dir", analysis_dir)
r.set_param("J", (500, 10000))
r.run(sims=1000, ipyclient=ipyclient)
Project directory exists. Additional simulations will be appended. Generating 1000 simulation(s). [############### ] 77% Performing Simulations | 1:12:38 |
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
[################# ] 86% Performing Simulations | 1:21:43 |
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
[################### ] 97% Performing Simulations | 1:30:35 |
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
[################### ] 99% Performing Simulations | 1:39:10 |
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
[################### ] 99% Performing Simulations | 1:47:45 |
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
[################### ] 99% Performing Simulations | 1:55:51 |
IOPub message rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_msg_rate_limit`. Current values: NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec) NotebookApp.rate_limit_window=3.0 (secs)
Now that the simulations are complete lets take a peek at the results.
SIMOUT = "{}/SIMOUT.txt".format(analysis_dir)
sim_df = pd.read_csv(SIMOUT, sep="\t", header=0)
print("Nsims", len(sim_df))
('Nsims', 2018)
## Get rid of failed simulations
sim_df = sim_df.dropna()
## Select features. The first line here takes all abundance and pi hill numbers.
## The second line just takes hill 1 for abundance and pi
## The third line uses all of the sumstats
#features = [x for x in sim_df.columns if "_h" in x]
#features = ["abund_h1", "pi_h1"]
features = sim_df.iloc[:, 22:43].columns
## Parameters to estimate
targets = ["J", "speciation_rate", "m"]
X = sim_df[features]
y = sim_df[targets]
## Split the data
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)
## Set search ranges for all parameter values of interest
n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap}
## Randomly search 100 different parameter combinations and take the
## one that reduces CV error
rf_random = RandomizedSearchCV(estimator = RandomForestRegressor(),\
param_distributions = random_grid,
n_iter = 100, cv = 3, verbose=0, n_jobs = -1)
rf_random.fit(Xtrain, ytrain)
RandomizedSearchCV(cv=3, error_score='raise-deprecating', estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False), fit_params=None, iid='warn', n_iter=100, n_jobs=-1, param_distributions={'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'min_samples_split': [2, 5, 10], 'bootstrap': [True, False], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_leaf': [1, 2, 4]}, pre_dispatch='2*n_jobs', random_state=None, refit=True, return_train_score='warn', scoring=None, verbose=0)
rf_random.best_params_
{'bootstrap': True, 'max_depth': 50, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 1000}
The R2 here is really bad because speciation rate is hard to estimate. R2 scores for J and m are quite good. Species richness soaks up most of the feature importance, though so maybe it's an artifact for now.
model = rf_random.best_estimator_
ypred = model.predict(Xtest)
print(model.feature_importances_)
print(metrics.explained_variance_score(ypred, ytest))
print(metrics.r2_score(ypred, ytest))
[0.67037157 0.03174772 0.00803484 0.01106243 0.01703773 0.01319224 0.00734701 0.00579792 0.00696186 0.01837299 0.01328927 0.006608 0.00857947 0.00169399 0.00680843 0.01964178 0.01165979 0.013948 0.01816128 0.09580675 0.01387692] -0.5832846483081361 -0.591464282701672
scores = cross_val_score(model, X, y, cv=5, n_jobs=-1)
scores
array([0.74385796, 0.78934961, 0.71368041, 0.75296035, 0.79710002])
This is essentially the same to the good old ABC leave-one-out cross validation. cross_val_predict
does the CV for the given k-folds and returns not the cv score,
but the predicted value of each target when it was in the holdout set. FUck!
cv_preds = cross_val_predict(model, X, y, cv=5, n_jobs=-1)
cv_preds[:2]
array([[1.58394218e+03, 4.34889000e-04, 1.94062076e-02], [1.57098522e+03, 4.11938693e-04, 1.76391871e-02]])
for i, p, lims in zip([0, 1, 2], ["J", "speciation_rate", "m"],
[(0, 10000), (0, 0.001), (0, 0.05)]):
fig, ax = plt.subplots()
print(metrics.explained_variance_score(y.iloc[:, i], cv_preds[:, i]))
print(metrics.r2_score(y.iloc[:, i], cv_preds[:, i]))
ax.scatter(y.iloc[:, i], cv_preds[:, i])
ax.set_xlim(lims)
ax.set_ylim(lims)
0.7602582286003994 0.7602581993382237 0.2583793174635568 0.25833178894110753 0.7123621303004228 0.7122690594535066
## Helper function for getting an obs SGD as a list
def obs_sgd_to_np(infile, normed=True):
with open(infile) as indat:
dat = indat.readlines()[1]
dat = np.array([int(x.strip()) for x in dat.split() if x != " "])
if normed:
dat = dat/float(dat.sum())
return dat
obs_sgd_to_np("/home/isaac/SGD_empirical/empirical_snails/snail.obs")
array([0.26086957, 0.2173913 , 0.2173913 , 0.13043478, 0.06521739, 0.06521739, 0. , 0.02173913, 0. , 0.02173913])
obs_files = {"snails":"/home/isaac/SGD_empirical/empirical_snails/snail.obs",
"moths":"/home/isaac/SGD_empirical/empirical_micromoths/micromoth.obs",
"spider":"/home/isaac/SGD_empirical/empirical_reunion_spiders/spider.obs",
"reunion_weevil":"/home/isaac/SGD_empirical/empirical_weevils/reunion.obs",
"mauritius_weevil":"/home/isaac/SGD_empirical/empirical_weevils/mauritius.obs"}
obs_dat = {obs:obs_sgd_to_np(f) for obs, f in obs_files.items()}
print(obs_dat)
{'snails': array([0.26086957, 0.2173913 , 0.2173913 , 0.13043478, 0.06521739, 0.06521739, 0. , 0.02173913, 0. , 0.02173913]), 'moths': array([0.57843137, 0.14705882, 0.1372549 , 0.02941176, 0.04901961, 0.00980392, 0.01960784, 0.00980392, 0.00980392, 0.00980392]), 'spider': array([0.14035088, 0.43859649, 0.12280702, 0.07017544, 0.01754386, 0.05263158, 0.03508772, 0.07017544, 0.01754386, 0.03508772]), 'reunion_weevil': array([0.11111111, 0.14814815, 0.18518519, 0.14814815, 0.07407407, 0.14814815, 0.07407407, 0. , 0. , 0.11111111]), 'mauritius_weevil': array([0.15384615, 0.23076923, 0.07692308, 0.34615385, 0.03846154, 0.03846154, 0.03846154, 0. , 0.03846154, 0.03846154])}
import glob
aquatic_files = glob.glob("/home/isaac/SGD_empirical/empirical_aquatic_metagenome/aquatic_metagenome_obs/*.obs")
#aquatic_files[0].rsplit("/", 1)
aquatic_dat = {f.rsplit("/", 1)[1]:obs_sgd_to_np(f) for f in aquatic_files}
aquatic_dat
{'II-2008_1.obs': array([0.5333927 , 0.02404274, 0.08726625, 0.18967053, 0.11843277, 0.03116652, 0.01068566, 0.00178094, 0.00178094, 0.00178094]), 'III-2008_7.obs': array([0.43767573, 0.08059981, 0.27460169, 0.17994377, 0.01968135, 0.00656045, 0. , 0. , 0. , 0.00093721]), 'I-2013_1.obs': array([0.63530778, 0.05691057, 0.06620209, 0.12311266, 0.07200929, 0.03135889, 0.00813008, 0.00232288, 0.00232288, 0.00232288]), 'III-2011_4.obs': array([6.34328358e-01, 2.55302435e-02, 1.12333071e-01, 1.79890024e-01, 3.65278869e-02, 7.85545954e-03, 1.57109191e-03, 7.85545954e-04, 7.85545954e-04, 3.92772977e-04]), 'V-2011_1.obs': array([0.4212283 , 0.01869159, 0.08477971, 0.13284379, 0.19158879, 0.10480641, 0.03404539, 0.00734312, 0.00267023, 0.00200267]), 'IV-2012_4.obs': array([0.54561404, 0.1254386 , 0.15087719, 0.13333333, 0.02807018, 0.00701754, 0.00350877, 0.00263158, 0.00175439, 0.00175439]), 'II-2009_4.obs': array([0.60489796, 0.2522449 , 0.12734694, 0.0122449 , 0.00081633, 0. , 0.00163265, 0. , 0. , 0.00081633]), 'IV-2013_1.obs': array([0.60629921, 0.07637795, 0.09055118, 0.12834646, 0.06377953, 0.02440945, 0.00551181, 0.0015748 , 0.0015748 , 0.0015748 ]), 'III-2015_1.obs': array([6.94329184e-01, 1.40156754e-01, 1.51221761e-01, 8.29875519e-03, 2.30520977e-03, 1.38312586e-03, 0.00000000e+00, 1.38312586e-03, 4.61041955e-04, 4.61041955e-04]), 'III-2009_4.obs': array([0.60716656, 0.25945587, 0.12143331, 0.00729927, 0.00066357, 0.00066357, 0.00199071, 0. , 0.00066357, 0.00066357]), 'III-2010_1.obs': array([0.59223301, 0.04854369, 0.15898058, 0.14563107, 0.03883495, 0.00849515, 0.00121359, 0.00121359, 0.00364078, 0.00121359]), 'V-2008_7.obs': array([0.45454545, 0.02194357, 0.12016719, 0.21943574, 0.13270637, 0.03970742, 0.00835946, 0. , 0. , 0.0031348 ]), 'II-2010_4.obs': array([0.49832027, 0.10974244, 0.28331467, 0.09294513, 0.01007839, 0.00223964, 0.00111982, 0. , 0.00111982, 0.00111982]), 'III-2009_7.obs': array([0.48910752, 0.15671117, 0.17146873, 0.13352073, 0.03162333, 0.01335207, 0.00210822, 0.00140548, 0. , 0.00070274]), 'I-2011_7.obs': array([0.56988521, 0.07765024, 0.11883862, 0.14112086, 0.06549629, 0.01485483, 0.00540176, 0.00270088, 0.00067522, 0.0033761 ]), 'I-2011_4.obs': array([0.48707342, 0.02068252, 0.07342296, 0.09307135, 0.15305067, 0.11582213, 0.04653568, 0.00310238, 0.00517063, 0.00206825]), 'V-2014_7.obs': array([7.00192957e-01, 1.39170285e-01, 1.43270622e-01, 1.13362277e-02, 4.10033767e-03, 4.82392668e-04, 4.82392668e-04, 4.82392668e-04, 2.41196334e-04, 2.41196334e-04]), 'II-2013_4.obs': array([0.42350333, 0.04988914, 0.11862528, 0.11308204, 0.17960089, 0.07538803, 0.02217295, 0.00886918, 0.00554324, 0.00332594]), 'III-2012_4.obs': array([0.46098266, 0.02023121, 0.04190751, 0.10115607, 0.13294798, 0.1416185 , 0.07297688, 0.02095376, 0.00578035, 0.00144509]), 'I-2007_1.obs': array([0.44507361, 0.02718007, 0.09399773, 0.14156285, 0.16421291, 0.08493771, 0.03171008, 0.00453001, 0.0011325 , 0.00566251]), 'II-2011_1.obs': array([0.39253731, 0.02462687, 0.09477612, 0.19179104, 0.20522388, 0.06716418, 0.01865672, 0.00373134, 0.00074627, 0.00074627]), 'II-2012_1.obs': array([0.48623853, 0.16880734, 0.26788991, 0.06238532, 0.00733945, 0.00183486, 0.00183486, 0. , 0.00183486, 0.00183486]), 'III-2010_7.obs': array([0.48230354, 0.05578884, 0.21415717, 0.19196161, 0.0419916 , 0.00959808, 0.00239952, 0.00119976, 0. , 0.00059988]), 'I-2007_4.obs': array([0.47977685, 0.0460251 , 0.14783821, 0.23570432, 0.06694561, 0.0097629 , 0.0069735 , 0.0041841 , 0.0013947 , 0.0013947 ]), 'I-2012_4.obs': array([0.50180505, 0.0276775 , 0.08062575, 0.19735259, 0.15042118, 0.03008424, 0.00722022, 0. , 0.00240674, 0.00240674]), 'IV-2015_1.obs': array([6.92370461e-01, 1.61158711e-01, 1.30558956e-01, 9.38392493e-03, 3.67197062e-03, 8.15993472e-04, 4.07996736e-04, 4.07996736e-04, 8.15993472e-04, 4.07996736e-04]), 'V-2014_11.obs': array([0.6975945 , 0.16924399, 0.11597938, 0.01116838, 0.00257732, 0.00085911, 0.00085911, 0.00085911, 0. , 0.00085911]), 'III-2011_7.obs': array([0.49695122, 0.05487805, 0.13109756, 0.2398374 , 0.05894309, 0.01219512, 0.00406504, 0.00050813, 0.00101626, 0.00050813]), 'V-2010_7.obs': array([0.55569782, 0.08130602, 0.09667093, 0.13636364, 0.0915493 , 0.01984635, 0.01024328, 0.00448143, 0.00192061, 0.00192061]), 'IV-2010_7.obs': array([0.51077313, 0.02408112, 0.08998733, 0.15842839, 0.12927757, 0.05830165, 0.02027883, 0.00253485, 0.00443599, 0.00190114]), 'I-2013_7.obs': array([0.45598349, 0.06396149, 0.16574966, 0.24277854, 0.05845942, 0.00894085, 0.00068776, 0.00206327, 0.00068776, 0.00068776]), 'V-2010_1.obs': array([0.56685499, 0.08851224, 0.21563089, 0.10546139, 0.01506591, 0.0047081 , 0. , 0.00188324, 0. , 0.00188324]), 'II-2010_1.obs': array([0.5975976 , 0.07407407, 0.07807808, 0.10910911, 0.07907908, 0.03403403, 0.01501502, 0.00600601, 0.004004 , 0.003003 ]), 'V-2011_4.obs': array([0.5097423 , 0.02639849, 0.11565053, 0.16844752, 0.10056568, 0.06348209, 0.0106851 , 0.00251414, 0.00125707, 0.00125707]), 'II-2013_7.obs': array([0.47032307, 0.03681443, 0.08264463, 0.24267468, 0.1419985 , 0.02103681, 0.00300526, 0. , 0.00075131, 0.00075131]), 'III-2009_1.obs': array([0.62361624, 0.15276753, 0.11143911, 0.07601476, 0.02730627, 0.00221402, 0.00073801, 0.00147601, 0.00295203, 0.00147601]), 'IV-2010_1.obs': array([0.52808989, 0.06482282, 0.16594641, 0.19360415, 0.03802939, 0.00605013, 0.00172861, 0.0008643 , 0. , 0.0008643 ]), 'V-2012_7.obs': array([0.47559709, 0.03426791, 0.06957425, 0.22793354, 0.1588785 , 0.02440291, 0.00726895, 0.00155763, 0. , 0.00051921]), 'IV-2007_7.obs': array([6.75966448e-01, 2.29759300e-02, 4.88694384e-02, 9.84682713e-02, 1.07585704e-01, 2.97228301e-02, 1.09409190e-02, 3.82932166e-03, 1.09409190e-03, 5.47045952e-04]), 'I-2009_1_1.obs': array([0.41243582, 0.02053622, 0.0867085 , 0.14603537, 0.21163719, 0.09640616, 0.02110667, 0.00285225, 0.00171135, 0.00057045]), 'IV-2014_4.obs': array([6.68246445e-01, 1.53351388e-01, 1.57075152e-01, 1.35409614e-02, 2.03114421e-03, 1.35409614e-03, 3.38524035e-04, 1.69262018e-03, 2.03114421e-03, 3.38524035e-04]), 'V-2008_1.obs': array([0.51165049, 0.04951456, 0.10873786, 0.21262136, 0.09320388, 0.01747573, 0.00194175, 0.00291262, 0. , 0.00194175]), 'V-2009_4.obs': array([0.6326062 , 0.14293915, 0.14580941, 0.06314581, 0.00574053, 0.00459242, 0.00172216, 0.00114811, 0.00114811, 0.00114811]), 'I-2009_1.obs': array([0.57686883, 0.2425952 , 0.13540197, 0.03385049, 0.00352609, 0.00352609, 0.00141044, 0.00141044, 0.00070522, 0.00070522]), 'I-2010_1_1.obs': array([0.46166307, 0.0674946 , 0.10853132, 0.2224622 , 0.10367171, 0.02321814, 0.00701944, 0.00323974, 0.00161987, 0.00107991]), 'V-2013_1.obs': array([0.47757848, 0.02802691, 0.07735426, 0.10986547, 0.16367713, 0.10986547, 0.02130045, 0.0044843 , 0.0044843 , 0.00336323]), 'I-2008_1.obs': array([0.55211025, 0.04306632, 0.17398794, 0.17054264, 0.04392765, 0.00775194, 0.00344531, 0.00172265, 0.00086133, 0.00258398]), 'IV-2008_7.obs': array([0.48015123, 0.07561437, 0.14272212, 0.2173913 , 0.06427221, 0.01323251, 0.00283554, 0.00094518, 0. , 0.00283554]), 'I-2012_1_1.obs': array([0.47399605, 0.05924951, 0.10599078, 0.20605662, 0.11652403, 0.03028308, 0.00658328, 0.00065833, 0. , 0.00065833]), 'IV-2013_4.obs': array([0.595724 , 0.01846453, 0.08940719, 0.12244898, 0.11078717, 0.04275996, 0.00874636, 0.00680272, 0.00194363, 0.00291545]), 'V-2013_7.obs': array([0.51286655, 0.01855177, 0.05625374, 0.10233393, 0.16217834, 0.10951526, 0.02812687, 0.005386 , 0.00418911, 0.00059844]), 'II-2007_7.obs': array([0.48902821, 0.01645768, 0.08855799, 0.19670846, 0.14655172, 0.04545455, 0.01410658, 0.0007837 , 0.0007837 , 0.0015674 ]), 'IV-2009_4.obs': array([6.26244874e-01, 1.62272994e-01, 1.41769186e-01, 5.33099004e-02, 1.17164616e-02, 2.92911541e-03, 0.00000000e+00, 0.00000000e+00, 1.17164616e-03, 5.85823081e-04]), 'IV-2011_1.obs': array([0.53694581, 0.11189303, 0.10626319, 0.13863476, 0.08092892, 0.0140746 , 0.00492611, 0.00422238, 0.00070373, 0.00140746]), 'IV-2012_1_1.obs': array([0.47827939, 0.03534923, 0.08560477, 0.24063032, 0.12223169, 0.02512777, 0.00851789, 0.00170358, 0.00170358, 0.00085179]), 'II-2009_1_1.obs': array([6.30998345e-01, 8.38389410e-02, 9.04578047e-02, 1.04247104e-01, 6.56370656e-02, 1.82018753e-02, 3.30943188e-03, 2.75785990e-03, 0.00000000e+00, 5.51571980e-04]), 'V-2012_1.obs': array([0.45636998, 0.08027923, 0.19982548, 0.20418848, 0.04973822, 0.0061082 , 0.0008726 , 0.0008726 , 0.0008726 , 0.0008726 ]), 'IV-2010_1_1.obs': array([6.51561310e-01, 6.09291698e-02, 1.17669459e-01, 1.27570449e-01, 2.89413557e-02, 4.56968774e-03, 4.18888043e-03, 3.04645849e-03, 3.80807312e-04, 1.14242193e-03]), 'V-2009_1.obs': array([0.62832447, 0.12433511, 0.14494681, 0.08045213, 0.01263298, 0.00398936, 0.00332447, 0.00066489, 0.00066489, 0.00066489]), 'I-2010_4.obs': array([0.47600519, 0.02594034, 0.0920882 , 0.2075227 , 0.1309987 , 0.05058366, 0.00907912, 0.00389105, 0. , 0.00389105]), 'II-2010_1_1.obs': array([0.46173689, 0.05760963, 0.16680997, 0.23129837, 0.06018917, 0.01375752, 0.00601892, 0.00085985, 0.00085985, 0.00085985]), 'I-2014_4.obs': array([6.66135458e-01, 1.45816733e-01, 1.62549801e-01, 1.91235060e-02, 7.96812749e-04, 2.39043825e-03, 1.59362550e-03, 1.19521912e-03, 0.00000000e+00, 3.98406375e-04]), 'V-2013_4.obs': array([0.40842788, 0.05834684, 0.10372771, 0.20745543, 0.14262561, 0.05996759, 0.01134522, 0.00486224, 0.00162075, 0.00162075]), 'IV-2012_7.obs': array([0.46299639, 0.02978339, 0.09837545, 0.21299639, 0.12635379, 0.0532491 , 0.0099278 , 0.00361011, 0.00180505, 0.00090253]), 'III-2008_1.obs': array([0.56279962, 0.063279 , 0.10450623, 0.19175455, 0.06232023, 0.00862895, 0.00287632, 0.00191755, 0. , 0.00191755]), 'III-2013_4.obs': array([0.63401721, 0.10059563, 0.07809398, 0.11317009, 0.049636 , 0.01654533, 0.00463269, 0.00066181, 0.00132363, 0.00132363]), 'II-2014_7.obs': array([7.03276836e-01, 1.45536723e-01, 1.34237288e-01, 1.10734463e-02, 2.25988701e-03, 9.03954802e-04, 1.12994350e-03, 9.03954802e-04, 2.25988701e-04, 4.51977401e-04]), 'I-2011_1.obs': array([0.43422354, 0.01681503, 0.10484669, 0.17507418, 0.15825915, 0.08308605, 0.01384768, 0.00791296, 0.00395648, 0.00197824]), 'III-2012_1.obs': array([0.61239193, 0.13112392, 0.1167147 , 0.10806916, 0.01729107, 0.00720461, 0.00288184, 0.00144092, 0.00144092, 0.00144092]), 'III-2012_1_1.obs': array([0.53576341, 0.07015131, 0.08390646, 0.08046768, 0.11210454, 0.07290234, 0.03026135, 0.00825309, 0.00137552, 0.00481431]), 'IV-2009_7.obs': array([0.48209366, 0.12304867, 0.17998163, 0.1533517 , 0.04407713, 0.00826446, 0.00459137, 0.00183655, 0.00183655, 0.00091827]), 'V-2010_4.obs': array([0.45276873, 0.08143322, 0.11074919, 0.21009772, 0.09283388, 0.01954397, 0.01302932, 0.00814332, 0.00651466, 0.00488599]), 'II-2009_7.obs': array([0.45609319, 0.18458781, 0.19354839, 0.12992832, 0.02508961, 0.00537634, 0.00268817, 0. , 0.00089606, 0.00179211]), 'V-2007_4.obs': array([0.39728097, 0.04833837, 0.1858006 , 0.26586103, 0.07401813, 0.01963746, 0.00453172, 0. , 0.00151057, 0.00302115]), 'V-2014_4.obs': array([6.49251005e-01, 1.55279503e-01, 1.72086226e-01, 1.53452685e-02, 1.09609061e-03, 1.82681768e-03, 2.92290829e-03, 7.30727073e-04, 1.09609061e-03, 3.65363537e-04]), 'V-2007_1.obs': array([0.43991098, 0.04228487, 0.13130564, 0.25 , 0.12017804, 0.0111276 , 0.0037092 , 0.00074184, 0. , 0.00074184]), 'V-2011_7.obs': array([0.54425363, 0.06274769, 0.20937913, 0.16116248, 0.01717305, 0.00198151, 0.0006605 , 0.001321 , 0.0006605 , 0.0006605 ]), 'II-2012_4.obs': array([0.54559271, 0.09726444, 0.08510638, 0.18844985, 0.05319149, 0.01215805, 0.00759878, 0.00607903, 0.00303951, 0.00151976]), 'I-2014_11.obs': array([0.66070097, 0.20208799, 0.12229679, 0.01118568, 0. , 0.00074571, 0.00074571, 0. , 0.00074571, 0.00149142]), 'II-2014_11.obs': array([6.88916105e-01, 1.85548071e-01, 1.09001837e-01, 1.10226577e-02, 2.44947949e-03, 0.00000000e+00, 1.22473974e-03, 1.22473974e-03, 0.00000000e+00, 6.12369871e-04]), 'III-2010_4.obs': array([0.50697211, 0.02589641, 0.07968127, 0.1125498 , 0.15438247, 0.07171315, 0.03685259, 0.00298805, 0.0059761 , 0.00298805]), 'III-2012_7.obs': array([0.54148936, 0.12925532, 0.12021277, 0.13723404, 0.05106383, 0.01276596, 0.00319149, 0.00319149, 0. , 0.00159574]), 'I-2015_1.obs': array([0.64 , 0.20408163, 0.12326531, 0.02367347, 0.00326531, 0.00244898, 0. , 0.00081633, 0.00081633, 0.00163265]), 'IV-2011_4.obs': array([0.51360843, 0.01931519, 0.0570676 , 0.09130817, 0.13784021, 0.11501317, 0.05179982, 0.01053556, 0.00175593, 0.00175593]), 'I-2013_4.obs': array([0.37238095, 0.09238095, 0.17809524, 0.26857143, 0.06761905, 0.01238095, 0.00285714, 0.00190476, 0.00190476, 0.00190476]), 'IV-2009_1.obs': array([0.63957597, 0.11248528, 0.11542992, 0.09010601, 0.02826855, 0.00530035, 0.00588928, 0.00176678, 0. , 0.00117786]), 'II-2008_7.obs': array([0.43243243, 0.0160473 , 0.11739865, 0.21621622, 0.1402027 , 0.06418919, 0.00929054, 0.00337838, 0. , 0.00084459]), 'IV-2007_4.obs': array([0.56708861, 0.13417722, 0.1443038 , 0.11476793, 0.02616034, 0.00421941, 0.00421941, 0.00253165, 0.00084388, 0.00168776]), 'III-2013_7.obs': array([0.53029316, 0.03127036, 0.0742671 , 0.20651466, 0.12638436, 0.0228013 , 0.00456026, 0.0019544 , 0. , 0.0019544 ]), 'IV-2007_1.obs': array([0.43996416, 0.0797491 , 0.28136201, 0.17114695, 0.01971326, 0.00537634, 0.00179211, 0. , 0. , 0.00089606]), 'I-2010_7.obs': array([0.49967084, 0.02172482, 0.08097433, 0.20013167, 0.13429888, 0.04081633, 0.0118499 , 0.00460829, 0.00329164, 0.00263331]), 'III-2007_7.obs': array([0.45632799, 0.0855615 , 0.28579917, 0.14795009, 0.01901367, 0.00475342, 0. , 0. , 0. , 0.00059418]), 'II-2007_4.obs': array([0.47406514, 0.06634499, 0.07840772, 0.22436671, 0.10735826, 0.02412545, 0.013269 , 0.00361882, 0.00482509, 0.00361882]), 'I-2012_7.obs': array([0.46462264, 0.02437107, 0.10455975, 0.18003145, 0.15880503, 0.04166667, 0.01886792, 0.00393082, 0.00157233, 0.00157233]), 'III-2014_7.obs': array([6.97291825e-01, 1.20982030e-01, 1.52872690e-01, 1.99949380e-02, 3.54340673e-03, 2.02480385e-03, 1.01240192e-03, 1.01240192e-03, 5.06200962e-04, 7.59301443e-04]), 'V-2010_1_1.obs': array([0.44395797, 0.02539405, 0.10945709, 0.19527145, 0.1497373 , 0.05691769, 0.00875657, 0.0061296 , 0.00262697, 0.00175131]), 'II-2012_7.obs': array([5.46229803e-01, 1.23877917e-01, 1.40035907e-01, 1.42280072e-01, 3.45601436e-02, 5.38599641e-03, 4.48833034e-03, 1.34649910e-03, 1.34649910e-03, 4.48833034e-04]), 'II-2012_1_1.obs': array([0.40520694, 0.01602136, 0.08544726, 0.1435247 , 0.2129506 , 0.11348465, 0.01602136, 0.00267023, 0.00333778, 0.00133511]), 'II-2015_1.obs': array([7.09500275e-01, 1.76825920e-01, 1.06534871e-01, 3.84404174e-03, 5.49148819e-04, 1.09829764e-03, 5.49148819e-04, 5.49148819e-04, 0.00000000e+00, 5.49148819e-04]), 'I-2009_4.obs': array([0.64494229, 0.15139172, 0.14528174, 0.04684318, 0.00475221, 0.00339443, 0. , 0.00203666, 0.00067889, 0.00067889]), 'V-2009_7.obs': array([0.51400966, 0.11690821, 0.152657 , 0.15942029, 0.04057971, 0.00869565, 0. , 0.00483092, 0.00193237, 0.00096618]), 'IV-2012_1.obs': array([0.59427208, 0.075179 , 0.09427208, 0.12410501, 0.07159905, 0.0274463 , 0.00596659, 0.00238663, 0.00357995, 0.00119332]), 'I-2008_7.obs': array([0.4699115 , 0.02654867, 0.11327434, 0.18849558, 0.1460177 , 0.04070796, 0.00973451, 0.00353982, 0. , 0.00176991]), 'II-2010_7.obs': array([0.49291785, 0.05665722, 0.23512748, 0.17776204, 0.02903683, 0.00495751, 0.00070822, 0.00212465, 0. , 0.00070822]), 'II-2013_1.obs': array([0.47410817, 0.02301496, 0.06329114, 0.07940161, 0.17376295, 0.12773303, 0.03912543, 0.00345224, 0.01035673, 0.00575374]), 'IV-2009_1_1.obs': array([0.51196411, 0.05483549, 0.16101695, 0.20737787, 0.04985045, 0.00947159, 0.00149551, 0.00099701, 0.00199402, 0.00099701]), 'IV-2011_7.obs': array([0.4223176 , 0.03261803, 0.11759657, 0.22575107, 0.14248927, 0.04206009, 0.00944206, 0.00343348, 0.00257511, 0.00171674]), 'III-2010_1_1.obs': array([0.42392127, 0.02573808, 0.10219531, 0.14307343, 0.18849357, 0.08024224, 0.02573808, 0.00378501, 0.00529902, 0.001514 ]), 'V-2015_1.obs': array([6.80722892e-01, 1.63989290e-01, 1.38554217e-01, 1.00401606e-02, 2.00803213e-03, 1.33868809e-03, 6.69344043e-04, 1.33868809e-03, 0.00000000e+00, 1.33868809e-03]), 'V-2007_7.obs': array([5.60772040e-01, 4.27751695e-02, 1.86228482e-01, 1.74752217e-01, 2.60824204e-02, 5.21648409e-03, 1.56494523e-03, 1.56494523e-03, 5.21648409e-04, 5.21648409e-04]), 'IV-2008_1.obs': array([0.50892019, 0.0685446 , 0.24131455, 0.14929577, 0.02253521, 0.00375587, 0.0028169 , 0.00093897, 0.00093897, 0.00093897]), 'III-2014_4.obs': array([0.67833109, 0.1444594 , 0.15163751, 0.01525348, 0.00448632, 0.0013459 , 0.00089726, 0.00179453, 0.00089726, 0.00089726]), 'II-2011_7.obs': array([0.49031396, 0.05344021, 0.19038076, 0.1990648 , 0.04475618, 0.01336005, 0.00534402, 0.000668 , 0.000668 , 0.00200401]), 'III-2007_4.obs': array([0.43090639, 0.02674591, 0.08320951, 0.23476969, 0.16196137, 0.04457652, 0.01188707, 0.00148588, 0.00297177, 0.00148588]), 'IV-2013_7.obs': array([0.45957194, 0.04458977, 0.08442331, 0.20749108, 0.16171225, 0.02913199, 0.00891795, 0.00178359, 0.00118906, 0.00118906]), 'IV-2010_4.obs': array([0.57 , 0.12181818, 0.16727273, 0.11363636, 0.01818182, 0.00363636, 0.00181818, 0.00272727, 0. , 0.00090909]), 'II-2007_1.obs': array([0.3858885 , 0.06533101, 0.22038328, 0.26916376, 0.05400697, 0.00174216, 0.00174216, 0.00087108, 0. , 0.00087108]), 'IV-2014_11.obs': array([6.86450168e-01, 1.19820829e-01, 1.74692049e-01, 1.28779395e-02, 2.23964166e-03, 1.67973124e-03, 5.59910414e-04, 0.00000000e+00, 0.00000000e+00, 1.67973124e-03]), 'I-2014_7.obs': array([6.89696606e-01, 1.56803845e-01, 1.36978071e-01, 1.05136678e-02, 3.00390508e-03, 3.00390508e-04, 9.01171523e-04, 3.00390508e-04, 9.01171523e-04, 6.00781015e-04]), 'II-2014_4.obs': array([6.71397380e-01, 1.20087336e-01, 1.78675400e-01, 2.18340611e-02, 2.54730713e-03, 1.81950509e-03, 3.63901019e-04, 2.18340611e-03, 7.27802038e-04, 3.63901019e-04]), 'III-2007_1.obs': array([0.53667055, 0.16763679, 0.16880093, 0.09720605, 0.0209546 , 0.00174622, 0.00232829, 0.00349243, 0.00058207, 0.00058207]), 'I-2010_1.obs': array([0.5768057 , 0.01831129, 0.05493388, 0.07324517, 0.11698881, 0.08850458, 0.04476094, 0.01831129, 0.00406918, 0.00406918]), 'V-2012_1_1.obs': array([0.51133333, 0.01333333, 0.05066667, 0.09933333, 0.16133333, 0.122 , 0.03066667, 0.00733333, 0.00266667, 0.00133333]), 'I-2012_1.obs': array([0.62427746, 0.07514451, 0.13294798, 0.13294798, 0.01734104, 0.01156069, 0.00385356, 0. , 0. , 0.00192678]), 'III-2011_1.obs': array([0.6486074 , 0.06219 , 0.09576498, 0.11484166, 0.05913773, 0.00915681, 0.00419687, 0.0034338 , 0.0011446 , 0.00152614]), 'III-2014_11.obs': array([6.91092862e-01, 1.09286166e-01, 1.64876816e-01, 2.77953253e-02, 3.15855970e-03, 1.26342388e-03, 0.00000000e+00, 6.31711939e-04, 0.00000000e+00, 1.89513582e-03]), 'V-2012_4.obs': array([0.45119306, 0.02928416, 0.09219089, 0.12689805, 0.19414317, 0.07375271, 0.02169197, 0.00759219, 0.0010846 , 0.0021692 ]), 'IV-2014_7.obs': array([6.78609914e-01, 1.63793103e-01, 1.41163793e-01, 9.42887931e-03, 2.42456897e-03, 1.61637931e-03, 8.08189655e-04, 1.61637931e-03, 2.69396552e-04, 2.69396552e-04]), 'V-2009_1_1.obs': array([0.51937536, 0.01966455, 0.09253904, 0.19838057, 0.12261423, 0.03238866, 0.0092539 , 0.00231348, 0.00231348, 0.00115674]), 'II-2011_4.obs': array([0.46070461, 0.0501355 , 0.15447154, 0.26829268, 0.05826558, 0.00271003, 0.00406504, 0. , 0. , 0.00135501]), 'II-2009_1.obs': array([0.60498221, 0.18362989, 0.13736655, 0.05551601, 0.00711744, 0.00355872, 0.00142349, 0.00213523, 0.00071174, 0.00355872]), 'I-2007_7.obs': array([0.44957386, 0.05397727, 0.11221591, 0.24147727, 0.11363636, 0.02272727, 0.00355114, 0. , 0.00213068, 0.00071023]), 'I-2009_7.obs': array([0.53297546, 0.11886503, 0.15030675, 0.13880368, 0.04064417, 0.01226994, 0.00306748, 0.00230061, 0. , 0.00076687]), 'III-2009_1_1.obs': array([0.47813195, 0.02668643, 0.08154188, 0.11415864, 0.18087472, 0.07635285, 0.03187546, 0.00518903, 0.00370645, 0.00148258])}
A_files = glob.glob("/home/isaac/ABCReefs/fastas/*/Anomura.obs")
A_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=False) for f in A_files}
A_richness = {name:np.sum(bins) for name, bins in A_dat.items()}
A_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=True) for f in A_files}
display(A_richness)
display(A_dat)
B_files = glob.glob("/home/isaac/ABCReefs/fastas/*/Brachyura.obs")
B_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=False) for f in B_files}
B_richness = {name:np.sum(bins) for name, bins in B_dat.items()}
B_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=True) for f in B_files}
display(B_richness)
display(B_dat)
C_files = glob.glob("/home/isaac/ABCReefs/fastas/*/Caridea.obs")
C_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=False) for f in C_files}
C_richness = {name:np.sum(bins) for name, bins in C_dat.items()}
C_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=True) for f in C_files}
display(C_richness)
display(C_dat)
ABC_files = glob.glob("/home/isaac/ABCReefs/fastas/*/Combined.obs")
ABC_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=False) for f in ABC_files}
ABC_richness = {name:np.sum(bins) for name, bins in ABC_dat.items()}
ABC_dat = {f.rsplit("/", 2)[1]:obs_sgd_to_np(f, normed=True) for f in ABC_files}
display(ABC_richness)
display(ABC_dat)
{'Pemuteran': 71, 'Kalimantan': 27, 'Lombok': 35, 'Solor': 32, 'Lembongan': 20, 'RajaAmpat': 41, 'Karimunjawa': 10, 'BarangLompo': 16, 'Manado': 22, 'Aceh': 39}
{'Pemuteran': array([0.4084507 , 0.08450704, 0.07042254, 0.14084507, 0.15492958, 0.07042254, 0.02816901, 0. , 0.01408451, 0.02816901]), 'Kalimantan': array([0.37037037, 0. , 0.14814815, 0.07407407, 0.11111111, 0.14814815, 0. , 0.03703704, 0.03703704, 0.07407407]), 'Lombok': array([0.34285714, 0.14285714, 0.17142857, 0.11428571, 0.17142857, 0. , 0. , 0.02857143, 0. , 0.02857143]), 'Solor': array([0.4375 , 0.0625 , 0.09375, 0.15625, 0.03125, 0.0625 , 0.0625 , 0.03125, 0.03125, 0.03125]), 'Lembongan': array([0.4 , 0.05, 0. , 0.15, 0. , 0.15, 0.1 , 0. , 0.1 , 0.05]), 'RajaAmpat': array([0.34146341, 0.12195122, 0.07317073, 0.12195122, 0.09756098, 0.12195122, 0.04878049, 0.02439024, 0. , 0.04878049]), 'Karimunjawa': array([0.3, 0.2, 0. , 0.1, 0.1, 0.2, 0. , 0. , 0. , 0.1]), 'BarangLompo': array([0.4375, 0.0625, 0.125 , 0. , 0.0625, 0.0625, 0.125 , 0. , 0.0625, 0.0625]), 'Manado': array([0.36363636, 0. , 0.04545455, 0.09090909, 0.22727273, 0.04545455, 0.09090909, 0. , 0.09090909, 0.04545455]), 'Aceh': array([0.43589744, 0.05128205, 0.20512821, 0.1025641 , 0. , 0.1025641 , 0.05128205, 0.02564103, 0. , 0.02564103])}
{'Pemuteran': 118, 'Kalimantan': 51, 'Lombok': 46, 'Solor': 68, 'Lembongan': 44, 'RajaAmpat': 64, 'Karimunjawa': 26, 'BarangLompo': 32, 'Manado': 49, 'Aceh': 68}
{'Pemuteran': array([0.53389831, 0.13559322, 0.1440678 , 0.00847458, 0.04237288, 0.06779661, 0.02542373, 0.00847458, 0.00847458, 0.02542373]), 'Kalimantan': array([0.56862745, 0.11764706, 0.11764706, 0.01960784, 0.05882353, 0.03921569, 0.03921569, 0.01960784, 0. , 0.01960784]), 'Lombok': array([0.65217391, 0.13043478, 0.10869565, 0.04347826, 0.02173913, 0. , 0.02173913, 0. , 0. , 0.02173913]), 'Solor': array([0.57352941, 0.11764706, 0.13235294, 0.05882353, 0.05882353, 0.01470588, 0. , 0.01470588, 0.01470588, 0.01470588]), 'Lembongan': array([0.5 , 0.20454545, 0.09090909, 0.06818182, 0.06818182, 0.02272727, 0. , 0. , 0. , 0.04545455]), 'RajaAmpat': array([0.609375, 0.078125, 0.109375, 0.046875, 0.046875, 0.03125 , 0.046875, 0. , 0.015625, 0.015625]), 'Karimunjawa': array([0.53846154, 0.07692308, 0.11538462, 0.03846154, 0.03846154, 0.03846154, 0.07692308, 0.03846154, 0. , 0.03846154]), 'BarangLompo': array([0.71875, 0.0625 , 0.125 , 0.03125, 0. , 0. , 0.03125, 0. , 0. , 0.03125]), 'Manado': array([0.57142857, 0.14285714, 0.08163265, 0.08163265, 0.02040816, 0.04081633, 0.04081633, 0. , 0. , 0.02040816]), 'Aceh': array([0.58823529, 0.10294118, 0.04411765, 0.10294118, 0.05882353, 0.04411765, 0. , 0. , 0.01470588, 0.04411765])}
{'Pemuteran': 110, 'Kalimantan': 44, 'Lombok': 58, 'Solor': 57, 'Lembongan': 32, 'RajaAmpat': 66, 'Karimunjawa': 29, 'BarangLompo': 48, 'Manado': 39, 'Aceh': 74}
{'Pemuteran': array([0.41818182, 0.16363636, 0.11818182, 0.12727273, 0.05454545, 0.03636364, 0.02727273, 0.03636364, 0. , 0.01818182]), 'Kalimantan': array([0.40909091, 0.13636364, 0.02272727, 0.11363636, 0.09090909, 0.06818182, 0.06818182, 0.02272727, 0. , 0.06818182]), 'Lombok': array([0.56896552, 0.15517241, 0.13793103, 0.05172414, 0. , 0.01724138, 0. , 0.03448276, 0.01724138, 0.01724138]), 'Solor': array([0.63157895, 0.10526316, 0.12280702, 0.03508772, 0.03508772, 0. , 0.03508772, 0. , 0.01754386, 0.01754386]), 'Lembongan': array([0.5 , 0.1875 , 0.09375, 0.09375, 0. , 0. , 0.03125, 0.0625 , 0. , 0.03125]), 'RajaAmpat': array([0.5 , 0.09090909, 0.15151515, 0.09090909, 0.04545455, 0.07575758, 0.01515152, 0. , 0.01515152, 0.01515152]), 'Karimunjawa': array([0.4137931 , 0.24137931, 0.17241379, 0.06896552, 0.03448276, 0.03448276, 0. , 0. , 0. , 0.03448276]), 'BarangLompo': array([0.3125 , 0.08333333, 0.14583333, 0.20833333, 0.125 , 0.0625 , 0.02083333, 0. , 0. , 0.04166667]), 'Manado': array([0.56410256, 0.07692308, 0.1025641 , 0.12820513, 0.05128205, 0.02564103, 0.02564103, 0. , 0. , 0.02564103]), 'Aceh': array([0.37837838, 0.18918919, 0.12162162, 0.08108108, 0.10810811, 0.09459459, 0. , 0.01351351, 0. , 0.01351351])}
{'Pemuteran': 283, 'Kalimantan': 118, 'Lombok': 127, 'Solor': 152, 'Lembongan': 93, 'RajaAmpat': 168, 'Karimunjawa': 62, 'BarangLompo': 92, 'Manado': 104, 'Aceh': 172}
{'Pemuteran': array([0.43462898, 0.19434629, 0.08833922, 0.12367491, 0.06007067, 0.04946996, 0.01766784, 0.01766784, 0. , 0.01413428]), 'Kalimantan': array([0.44067797, 0.11864407, 0.15254237, 0.05932203, 0.10169492, 0.04237288, 0.03389831, 0.02542373, 0.00847458, 0.01694915]), 'Lombok': array([0.49606299, 0.16535433, 0.15748031, 0.06299213, 0.03937008, 0.02362205, 0.01574803, 0.02362205, 0. , 0.01574803]), 'Solor': array([0.55263158, 0.15131579, 0.10526316, 0.09210526, 0.00657895, 0.03947368, 0.01973684, 0.01315789, 0.01315789, 0.00657895]), 'Lembongan': array([0.46236559, 0.20430108, 0.11827957, 0.06451613, 0.04301075, 0.02150538, 0.03225806, 0.03225806, 0.01075269, 0.01075269]), 'RajaAmpat': array([0.49404762, 0.13690476, 0.10714286, 0.08928571, 0.07142857, 0.05357143, 0.01190476, 0.01190476, 0.01785714, 0.00595238]), 'Karimunjawa': array([0.41935484, 0.22580645, 0.11290323, 0.09677419, 0.08064516, 0.03225806, 0. , 0. , 0.01612903, 0.01612903]), 'BarangLompo': array([0.44565217, 0.11956522, 0.10869565, 0.11956522, 0.07608696, 0.07608696, 0.01086957, 0.01086957, 0. , 0.0326087 ]), 'Manado': array([0.49038462, 0.14423077, 0.16346154, 0.08653846, 0.06730769, 0.01923077, 0.01923077, 0. , 0. , 0.00961538]), 'Aceh': array([0.44767442, 0.15116279, 0.13953488, 0.0872093 , 0.06395349, 0.05813953, 0.02906977, 0.01162791, 0. , 0.01162791])}
neut_df = pd.read_csv("neutral_MESS/SIMOUT.txt", sep="\t", header=0)
filt_df = pd.read_csv("filtering_MESS/SIMOUT.txt", sep="\t", header=0)
comp_df = pd.read_csv("competition_MESS/SIMOUT.txt", sep="\t", header=0)
## Label and merge the data frames of each model for classification
neut_df = neut_df.assign(model=["neutral"] * len(neut_df))
filt_df = filt_df.assign(model=["filtering"] * len(filt_df))
comp_df = comp_df.assign(model=["competition"] * len(comp_df))
full_df = pd.concat([neut_df, filt_df, comp_df])
df_dict = {"neutral":neut_df, "filtering":filt_df, "competition":comp_df}
for lab, df in df_dict.items():
print("nsims {} {}".format(lab, len(df)))
nsims neutral 2997 nsims filtering 2996 nsims competition 2492
print(len(X))
neut_df.columns
5987
Index([u'_lambda', u'generation', u'K', u'colrate', u'speciation_probability', u'sigma', u'trait_rate', u'ecological_strength', u'filtering_optimum', u'colrate_calculated', u'extrate_calculated', u'R', u'shannon', u'mean_pi', u'stdv_pi', u'median_pi', u'iqr_pi', u'mean_dxy', u'stdv_dxy', u'median_dxy', u'iqr_dxy', u'trees', u'mn_local_traits', u'var_local_traits', u'mn_regional_traits', u'var_regional_traits', u'reg_loc_mn_trait_dif', u'reg_loc_var_trait_dif', u'kurtosis_local_traits', u'skewness_local_traits', u'SGD_0', u'SGD_1', u'SGD_2', u'SGD_3', u'SGD_4', u'SGD_5', u'SGD_6', u'SGD_7', u'SGD_8', u'SGD_9', u'model'], dtype='object')
def get_features_targets(df, normsgd=True, drop_0_bin=False, add_features=["shannon"], H1_feature=False, targets=["_lambda"]):
## Only use SGD. normalize sgd bins
X = df.filter(regex="SGD*")
if normsgd:
X = X.div(X.sum(axis=1), axis=0)
if H1_feature:
X = X.assign(shannon=df["shannon"])
X["shannon"] = X["shannon"].apply(np.exp)/df["R"]
if drop_0_bin:
X = X.drop(["SGD_0"], axis=1)
if len(add_features) > 0:
X[add_features] = df[add_features]
if not len(targets) > 1:
#y = df.filter(["shannon"]).values.ravel()
y = df.filter(targets).values.ravel()
else:
#y = df.filter(["_lambda", "shannon"])
y = df.filter(targets)
return X, y
def RF_regress(df, normsgd=True, add_features=[], drop_0_bin=False,\
H1_feature=True, power_transform=False, pca=False,\
targets=["_lambda"], cv=True, ax=[]):
X, y = get_features_targets(df, normsgd, drop_0_bin, add_features, H1_feature, targets)
if "shannon" in targets:
y = np.exp(y)/df["R"]
## Split the data
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)
pt = []
if power_transform:
pt = PowerTransformer()
#pt = StandardScaler()
#pt = QuantileTransformer()
Xtrain = pt.fit_transform(Xtrain)
Xtest = pt.fit_transform(Xtest)
elif pca:
pca = PCA(n_components=6)
Xtrain = pca.fit_transform(Xtrain)
Xtest = pca.transform(Xtest)
pt = pca
model = RandomForestRegressor(n_estimators=800, n_jobs=-1, max_depth=None,\
min_samples_split=5, min_samples_leaf=4, max_features='auto', bootstrap=True)
model.fit(Xtrain, ytrain)
ypred = model.predict(Xtest)
print(model.feature_importances_)
print(metrics.explained_variance_score(ypred, ytest))
print(metrics.r2_score(ypred, ytest))
if not ax:
fig, ax = plt.subplots(figsize=(10, 5))
plt.scatter(ypred, ytest)
return pt, model
normsgd = True
drop_0_bin = False
neutral = True
competition = False
filtering = False
if neutral:
lreg_pt, lregress_model = RF_regress(neut_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["_lambda"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
sreg_pt, sregress_model = RF_regress(neut_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["shannon"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
elif False:
lreg_pt, lregress_model = RF_regress(filt_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["_lambda"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
sreg_pt, sregress_model = RF_regress(filt_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["shannon"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
else:
lreg_pt, lregress_model = RF_regress(comp_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["_lambda"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
sreg_pt, sregress_model = RF_regress(comp_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["shannon"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
# greg_pt, gregress_model = RF_regress(comp_df, normsgd=normsgd, drop_0_bin=drop_0_bin, targets=["generation"], H1_feature=False, power_transform=True, pca=False)#, add_features=["var_local_traits"])
[0.04658749 0.05870905 0.05130648 0.04597719 0.03690692 0.04154725 0.10234626 0.01989087 0.02196608 0.5747624 ] 0.3078107677927938 0.3006582832373338 [0.05415422 0.07729036 0.09419913 0.06543115 0.04327809 0.05533874 0.05755175 0.0345608 0.02603472 0.49216104] 0.22576448787428716 0.22161878643411803
X, y = get_features_targets(neut_df)
scores = cross_val_score(regress_model, pt.transform(X), y, cv=10)
scores
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-10-46b4859fb53c> in <module> 1 X, y = get_features_targets(neut_df) ----> 2 scores = cross_val_score(regress_model, pt.transform(X), y, cv=10) 3 scores NameError: name 'regress_model' is not defined
for reg_pt, regress_model in zip([lreg_pt, sreg_pt], [lregress_model, sregress_model]):
for org, dat in obs_dat.items():
obs = reg_pt.transform([dat])
pred = regress_model.predict(obs)
print(org, pred)
print("Anomura")
for site, dat in A_dat.items():
obs = reg_pt.transform([dat])
pred = regress_model.predict(obs)
print(site, pred)
print("Brachyura")
for site, dat in B_dat.items():
obs = reg_pt.transform([dat])
pred = regress_model.predict(obs)
print(site, pred)
print("Caridea")
for site, dat in C_dat.items():
obs = reg_pt.transform([dat])
pred = regress_model.predict(obs)
print(site, pred)
print("Combined")
for site, dat in ABC_dat.items():
obs = reg_pt.transform([dat])
pred = regress_model.predict(obs)
print(site, pred)
snails [0.40114589] moths [0.26976356] spider [0.25762846] reunion_weevil [0.12685571] mauritius_weevil [0.17847066] Anomura Pemuteran [0.2680604] Kalimantan [0.11847579] Lombok [0.14808797] Solor [0.25745684] Lembongan [0.10302962] RajaAmpat [0.12016289] Karimunjawa [0.04506929] BarangLompo [0.11048239] Manado [0.12235496] Aceh [0.24585773] Brachyura Pemuteran [0.46311746] Kalimantan [0.27853631] Lombok [0.1822398] Solor [0.59917721] Lembongan [0.09800845] RajaAmpat [0.46160117] Karimunjawa [0.09353415] BarangLompo [0.11059909] Manado [0.45914727] Aceh [0.05444124] Caridea Pemuteran [0.36859415] Kalimantan [0.08976077] Lombok [0.24414255] Solor [0.2305122] Lembongan [0.1520202] RajaAmpat [0.63281023] Karimunjawa [0.17442566] BarangLompo [0.15646565] Manado [0.34745634] Aceh [0.4400875] Combined Pemuteran [0.70945318] Kalimantan [0.40115145] Lombok [0.74067751] Solor [0.8177508] Lembongan [0.16837311] RajaAmpat [0.81027884] Karimunjawa [0.48648183] BarangLompo [0.25458305] Manado [0.37476621] Aceh [0.14003003] snails [0.51950853] moths [0.53233457] spider [0.6960978] reunion_weevil [0.90309963] mauritius_weevil [0.76301177] Anomura Pemuteran [0.63772512] Kalimantan [0.81712693] Lombok [0.66262414] Solor [0.66137029] Lembongan [0.81101929] RajaAmpat [0.84735909] Karimunjawa [0.89279166] BarangLompo [0.76741029] Manado [0.79819128] Aceh [0.68757411] Brachyura Pemuteran [0.61599868] Kalimantan [0.65088381] Lombok [0.64750473] Solor [0.48622652] Lembongan [0.79280164] RajaAmpat [0.58741297] Karimunjawa [0.75444386] BarangLompo [0.7131081] Manado [0.56163828] Aceh [0.81769448] Caridea Pemuteran [0.50024819] Kalimantan [0.79762545] Lombok [0.57457022] Solor [0.62683588] Lembongan [0.65536946] RajaAmpat [0.5569173] Karimunjawa [0.64783925] BarangLompo [0.85878681] Manado [0.6318192] Aceh [0.49608263] Combined Pemuteran [0.43792224] Kalimantan [0.50796369] Lombok [0.45312487] Solor [0.43041431] Lembongan [0.5460043] RajaAmpat [0.44402027] Karimunjawa [0.49361506] BarangLompo [0.58200664] Manado [0.5090867] Aceh [0.56869028]
aquatic_regress_dat = {}
for org, dat in aquatic_dat.items():
obs = reg_pt.transform([dat])
#print(dat, obs)
pred = regress_model.predict(obs)
print(org, pred)
aquatic_regress_dat[org] = pred[0]
('IV-2012_1.obs', array([0.44247523])) ('II-2015_1.obs', array([0.44689924])) ('IV-2015_1.obs', array([0.45499756])) ('V-2013_4.obs', array([0.46233109])) ('I-2008_7.obs', array([0.45301133])) ('IV-2014_7.obs', array([0.45758787])) ('I-2012_1.obs', array([0.45435914])) ('I-2008_1.obs', array([0.46054259])) ('III-2015_1.obs', array([0.45584548])) ('II-2012_4.obs', array([0.4447602])) ('II-2010_4.obs', array([0.46961674])) ('IV-2012_7.obs', array([0.45590612])) ('III-2011_7.obs', array([0.45512112])) ('I-2011_7.obs', array([0.44900564])) ('I-2009_1.obs', array([0.46597071])) ('V-2009_7.obs', array([0.46110998])) ('V-2010_1_1.obs', array([0.45694004])) ('II-2010_7.obs', array([0.46719924])) ('IV-2007_4.obs', array([0.45520538])) ('V-2007_7.obs', array([0.46249021])) ('V-2011_7.obs', array([0.46726081])) ('III-2007_1.obs', array([0.47052046])) ('III-2009_7.obs', array([0.46574151])) ('II-2012_7.obs', array([0.45754581])) ('II-2011_4.obs', array([0.45686177])) ('I-2014_4.obs', array([0.45823786])) ('III-2013_4.obs', array([0.43964601])) ('I-2007_7.obs', array([0.44831116])) ('III-2014_11.obs', array([0.45590174])) ('II-2008_7.obs', array([0.46771119])) ('IV-2013_4.obs', array([0.43631696])) ('V-2009_4.obs', array([0.45870345])) ('II-2012_1_1.obs', array([0.46074153])) ('III-2014_4.obs', array([0.45740211])) ('IV-2013_1.obs', array([0.44138998])) ('V-2007_1.obs', array([0.46068286])) ('II-2007_7.obs', array([0.45049008])) ('V-2008_7.obs', array([0.45947335])) ('III-2011_4.obs', array([0.45127214])) ('IV-2009_1_1.obs', array([0.45696381])) ('V-2014_11.obs', array([0.45594448])) ('V-2009_1_1.obs', array([0.45005043])) ('IV-2012_4.obs', array([0.45747665])) ('III-2012_4.obs', array([0.44888263])) ('II-2009_7.obs', array([0.47456598])) ('II-2010_1.obs', array([0.43604087])) ('I-2010_1_1.obs', array([0.44946963])) ('V-2015_1.obs', array([0.4572143])) ('II-2011_7.obs', array([0.46426463])) ('V-2011_4.obs', array([0.45742495])) ('II-2010_1_1.obs', array([0.45912685])) ('IV-2009_7.obs', array([0.46561889])) ('V-2012_7.obs', array([0.44584998])) ('IV-2010_7.obs', array([0.45416406])) ('IV-2012_1_1.obs', array([0.45030363])) ('II-2014_11.obs', array([0.46014651])) ('III-2009_4.obs', array([0.44193741])) ('I-2013_4.obs', array([0.47572305])) ('I-2011_1.obs', array([0.45866699])) ('I-2012_1_1.obs', array([0.44781854])) ('III-2009_1.obs', array([0.45628585])) ('I-2010_1.obs', array([0.43564834])) ('II-2012_1.obs', array([0.47141894])) ('V-2010_7.obs', array([0.44573922])) ('II-2013_4.obs', array([0.46541181])) ('III-2012_1_1.obs', array([0.45297532])) ('II-2009_4.obs', array([0.44098162])) ('V-2012_4.obs', array([0.45660949])) ('IV-2007_1.obs', array([0.47128653])) ('V-2013_1.obs', array([0.45647766])) ('I-2007_4.obs', array([0.45797032])) ('V-2013_7.obs', array([0.45153431])) ('II-2013_7.obs', array([0.44758119])) ('V-2010_4.obs', array([0.45085625])) ('III-2010_1_1.obs', array([0.46256036])) ('III-2011_1.obs', array([0.44489159])) ('III-2007_4.obs', array([0.45735945])) ('III-2014_7.obs', array([0.4537343])) ('V-2010_1.obs', array([0.46265803])) ('III-2009_1_1.obs', array([0.45753529])) ('I-2010_4.obs', array([0.45517128])) ('III-2010_1.obs', array([0.45345792])) ('IV-2011_7.obs', array([0.46419052])) ('V-2012_1.obs', array([0.46574053])) ('III-2013_7.obs', array([0.44411455])) ('III-2012_1.obs', array([0.45152444])) ('V-2009_1.obs', array([0.45489606])) ('V-2008_1.obs', array([0.44609461])) ('V-2007_4.obs', array([0.47359053])) ('IV-2010_4.obs', array([0.45824675])) ('I-2015_1.obs', array([0.44486189])) ('IV-2008_7.obs', array([0.45721563])) ('III-2008_1.obs', array([0.44697102])) ('IV-2010_1.obs', array([0.46633893])) ('I-2009_7.obs', array([0.46182638])) ('II-2014_4.obs', array([0.4569507])) ('IV-2014_4.obs', array([0.4585737])) ('II-2013_1.obs', array([0.45933996])) ('I-2007_1.obs', array([0.45827752])) ('V-2014_7.obs', array([0.45396945])) ('I-2010_7.obs', array([0.44982943])) ('II-2014_7.obs', array([0.4542856])) ('I-2012_4.obs', array([0.44519748])) ('III-2007_7.obs', array([0.46985976])) ('V-2011_1.obs', array([0.4624099])) ('I-2009_4.obs', array([0.45731263])) ('III-2010_7.obs', array([0.46751038])) ('I-2013_7.obs', array([0.4606186])) ('IV-2009_1.obs', array([0.44867052])) ('IV-2008_1.obs', array([0.46735169])) ('II-2007_4.obs', array([0.45095726])) ('IV-2009_4.obs', array([0.45449849])) ('II-2009_1.obs', array([0.45844914])) ('IV-2011_4.obs', array([0.44767948])) ('I-2011_4.obs', array([0.4528554])) ('III-2010_4.obs', array([0.45380692])) ('III-2012_7.obs', array([0.45425377])) ('V-2012_1_1.obs', array([0.45048275])) ('I-2009_1_1.obs', array([0.46221294])) ('IV-2007_7.obs', array([0.43577166])) ('III-2008_7.obs', array([0.47436522])) ('II-2009_1_1.obs', array([0.44297705])) ('II-2008_1.obs', array([0.44850645])) ('II-2007_1.obs', array([0.47675804])) ('I-2014_11.obs', array([0.4485162])) ('IV-2014_11.obs', array([0.45678023])) ('I-2012_7.obs', array([0.45286096])) ('IV-2010_1_1.obs', array([0.45291133])) ('II-2011_1.obs', array([0.46329819])) ('IV-2011_1.obs', array([0.44607877])) ('I-2013_1.obs', array([0.43616105])) ('IV-2013_7.obs', array([0.44976487])) ('I-2014_7.obs', array([0.45628315])) ('V-2014_4.obs', array([0.46163261]))
def RF_classify(df, normsgd=True, add_features=[],\
H1_feature=False, power_transform=False, pca=False,\
cv=True, ax=[]):
X, y = get_features_targets(df, normsgd=normsgd, add_features=add_features, H1_feature=H1_feature, targets=["model"])
## Split the data
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)
pt = []
if power_transform:
pt = PowerTransformer()
#pt = StandardScaler()
#pt = QuantileTransformer()
Xtrain = pt.fit_transform(Xtrain)
Xtest = pt.fit_transform(Xtest)
elif pca:
pca = PCA(n_components=6)
Xtrain = pca.fit_transform(Xtrain)
Xtest = pca.transform(Xtest)
pt = pca
model = RandomForestClassifier(n_estimators=800, n_jobs=-1, min_samples_split=5, min_samples_leaf=4,\
max_features='auto', max_depth=None, bootstrap=True)
model.fit(Xtrain, ytrain)
ypred = model.predict(Xtest)
print(model.feature_importances_)
cm = metrics.confusion_matrix(ypred, ytest)
print(metrics.classification_report(ypred, ytest))
plot_confusion_matrix(cm, ["competition", "filtering", "neutral"], normalize=True)
return pt, model
#cla_pt, classify_model = RF_classify(full_df, normsgd=True, H1_feature=False, power_transform=True, add_features=["var_local_traits"])
cla_pt, classify_model = RF_classify(full_df, H1_feature=False, power_transform=True, normsgd=True)
[0.07754093 0.10118944 0.08485185 0.06539051 0.06238101 0.05725022 0.05318759 0.04662843 0.05512452 0.39645549] precision recall f1-score support competition 0.54 0.58 0.56 599 filtering 0.68 0.57 0.62 867 neutral 0.80 0.93 0.86 656 micro avg 0.68 0.68 0.68 2122 macro avg 0.68 0.69 0.68 2122 weighted avg 0.68 0.68 0.68 2122
for org, dat in obs_dat.items():
obs = cla_pt.transform([dat])
pred = classify_model.predict(obs)
pred_proba = classify_model.predict_proba(obs)
print(org, pred, pred_proba)
ABC_prob_df = {}
print("Anomura")
for site, dat in A_dat.items():
obs = cla_pt.transform([dat])
pred = classify_model.predict(obs)
pred_proba = classify_model.predict_proba(obs)
print(site, pred, pred_proba)
ABC_prob_df["Anomura-{}".format(site)] = pred_proba
print("Brachyura")
for site, dat in B_dat.items():
obs = cla_pt.transform([dat])
pred = classify_model.predict(obs)
pred_proba = classify_model.predict_proba(obs)
print(site, pred, pred_proba)
ABC_prob_df["Brachyura-{}".format(site)] = pred_proba
print("Caridea")
for site, dat in C_dat.items():
obs = cla_pt.transform([dat])
pred = classify_model.predict(obs)
pred_proba = classify_model.predict_proba(obs)
print(site, pred, pred_proba)
ABC_prob_df["Caridea-{}".format(site)] = pred_proba
print("Combined")
for site, dat in ABC_dat.items():
obs = cla_pt.transform([dat])
pred = classify_model.predict(obs)
pred_proba = classify_model.predict_proba(obs)
print(site, pred, pred_proba)
ABC_prob_df["Combined-{}".format(site)] = pred_proba
snails ['neutral'] [[0.30077942 0.31122017 0.38800041]] moths ['neutral'] [[0.01187013 0.0883172 0.89981267]] spider ['filtering'] [[0.33147982 0.43827518 0.230245 ]] reunion_weevil ['filtering'] [[0.30642699 0.38502901 0.308544 ]] mauritius_weevil ['competition'] [[0.47743906 0.34353938 0.17902157]] Anomura Pemuteran ['competition'] [[0.45296455 0.42394132 0.12309413]] Kalimantan ['competition'] [[0.3987495 0.3494937 0.2517568]] Lombok ['competition'] [[0.38849811 0.37279253 0.23870937]] Solor ['filtering'] [[0.4139439 0.41933633 0.16671977]] Lembongan ['competition'] [[0.43834882 0.42843758 0.1332136 ]] RajaAmpat ['competition'] [[0.54756985 0.29878203 0.15364812]] Karimunjawa ['filtering'] [[0.37058507 0.37798796 0.25142697]] BarangLompo ['filtering'] [[0.37172609 0.44610178 0.18217213]] Manado ['filtering'] [[0.42715947 0.44701497 0.12582555]] Aceh ['filtering'] [[0.34181614 0.41245438 0.24572948]] Brachyura Pemuteran ['filtering'] [[0.28634021 0.36764748 0.34601231]] Kalimantan ['competition'] [[0.48204719 0.45113146 0.06682135]] Lombok ['competition'] [[0.56871488 0.34194522 0.0893399 ]] Solor ['competition'] [[0.51791598 0.35272416 0.12935986]] Lembongan ['filtering'] [[0.37859873 0.44909054 0.17231073]] RajaAmpat ['competition'] [[0.45704035 0.44746662 0.09549303]] Karimunjawa ['competition'] [[0.46867495 0.41793317 0.11339187]] BarangLompo ['filtering'] [[0.40812221 0.53440641 0.05747138]] Manado ['filtering'] [[0.4313685 0.44981235 0.11881915]] Aceh ['filtering'] [[0.45456817 0.47519664 0.07023519]] Caridea Pemuteran ['neutral'] [[0.30033692 0.2902015 0.40946159]] Kalimantan ['competition'] [[0.51952514 0.3112421 0.16923276]] Lombok ['competition'] [[0.47433826 0.42894061 0.09672113]] Solor ['competition'] [[0.65201852 0.30964972 0.03833176]] Lembongan ['competition'] [[0.44979597 0.37763951 0.17256452]] RajaAmpat ['competition'] [[0.39437587 0.3845957 0.22102843]] Karimunjawa ['neutral'] [[0.33751427 0.21770662 0.44477911]] BarangLompo ['competition'] [[0.40108464 0.34457802 0.25433734]] Manado ['filtering'] [[0.24200706 0.63469095 0.12330199]] Aceh ['filtering'] [[0.40097403 0.45087261 0.14815336]] Combined Pemuteran ['filtering'] [[0.30543312 0.37638281 0.31818407]] Kalimantan ['neutral'] [[0.31485824 0.27421805 0.4109237 ]] Lombok ['neutral'] [[0.18621217 0.36032352 0.4534643 ]] Solor ['neutral'] [[0.01984201 0.1109528 0.8692052 ]] Lembongan ['neutral'] [[0.06305571 0.30244986 0.63449443]] RajaAmpat ['neutral'] [[0.03206273 0.07764064 0.89029663]] Karimunjawa ['neutral'] [[0.3304912 0.28739821 0.38211059]] BarangLompo ['filtering'] [[0.33954499 0.48576486 0.17469015]] Manado ['neutral'] [[0.01458057 0.05385 0.93156944]] Aceh ['filtering'] [[0.20687952 0.54362936 0.24949112]]
colors = ["red", "orange","blue"]
ABC_proba_df = {site:dat[0] for site, dat in ABC_prob_df.items()}
ABC_proba_df = pd.DataFrame.from_dict(ABC_proba_df, orient="index", columns=["competition", "filtering", "neutral"]).T
#display(ABC_proba_df)
for ABC in ["Anomura", "Brachyura", "Caridea", "Combined"]:
site = ABC_proba_df.filter(regex="{}-".format(ABC))
site = site.reindex(sorted(site.columns), axis=1)
display(site)
site.columns = [x.split("-")[1] for x in site.columns]
ax = site.T.loc[:,["competition", "filtering", "neutral"]].plot.bar(stacked=True, color=colors, figsize=(15,5), fontsize=25, legend=False)
ax.set_title("Infraorder {}".format(ABC), fontsize=20)
Anomura-Aceh | Anomura-BarangLompo | Anomura-Kalimantan | Anomura-Karimunjawa | Anomura-Lembongan | Anomura-Lombok | Anomura-Manado | Anomura-Pemuteran | Anomura-RajaAmpat | Anomura-Solor | |
---|---|---|---|---|---|---|---|---|---|---|
competition | 0.293414 | 0.355678 | 0.399652 | 0.331378 | 0.434366 | 0.350132 | 0.400672 | 0.446696 | 0.453084 | 0.413620 |
filtering | 0.492305 | 0.448168 | 0.354414 | 0.430096 | 0.425154 | 0.428189 | 0.461893 | 0.436846 | 0.402072 | 0.421247 |
neutral | 0.214281 | 0.196154 | 0.245934 | 0.238527 | 0.140480 | 0.221679 | 0.137435 | 0.116459 | 0.144845 | 0.165133 |
Brachyura-Aceh | Brachyura-BarangLompo | Brachyura-Kalimantan | Brachyura-Karimunjawa | Brachyura-Lembongan | Brachyura-Lombok | Brachyura-Manado | Brachyura-Pemuteran | Brachyura-RajaAmpat | Brachyura-Solor | |
---|---|---|---|---|---|---|---|---|---|---|
competition | 0.443895 | 0.434087 | 0.470151 | 0.391364 | 0.381764 | 0.581521 | 0.416663 | 0.244457 | 0.476221 | 0.501127 |
filtering | 0.481127 | 0.512799 | 0.465280 | 0.505870 | 0.444027 | 0.321514 | 0.450824 | 0.400599 | 0.443243 | 0.355265 |
neutral | 0.074978 | 0.053115 | 0.064569 | 0.102766 | 0.174209 | 0.096965 | 0.132513 | 0.354944 | 0.080537 | 0.143608 |
Caridea-Aceh | Caridea-BarangLompo | Caridea-Kalimantan | Caridea-Karimunjawa | Caridea-Lembongan | Caridea-Lombok | Caridea-Manado | Caridea-Pemuteran | Caridea-RajaAmpat | Caridea-Solor | |
---|---|---|---|---|---|---|---|---|---|---|
competition | 0.354467 | 0.328642 | 0.458799 | 0.344591 | 0.432174 | 0.526945 | 0.236770 | 0.310187 | 0.370806 | 0.658329 |
filtering | 0.519184 | 0.449664 | 0.381947 | 0.221809 | 0.399974 | 0.369202 | 0.668397 | 0.286075 | 0.417431 | 0.297856 |
neutral | 0.126349 | 0.221695 | 0.159253 | 0.433599 | 0.167852 | 0.103852 | 0.094833 | 0.403738 | 0.211763 | 0.043816 |
Combined-Aceh | Combined-BarangLompo | Combined-Kalimantan | Combined-Karimunjawa | Combined-Lembongan | Combined-Lombok | Combined-Manado | Combined-Pemuteran | Combined-RajaAmpat | Combined-Solor | |
---|---|---|---|---|---|---|---|---|---|---|
competition | 0.198621 | 0.298795 | 0.276490 | 0.321662 | 0.116934 | 0.187292 | 0.014052 | 0.319195 | 0.033616 | 0.019539 |
filtering | 0.592792 | 0.556125 | 0.296686 | 0.252589 | 0.340485 | 0.368672 | 0.057600 | 0.366595 | 0.055700 | 0.079085 |
neutral | 0.208587 | 0.145080 | 0.426824 | 0.425749 | 0.542581 | 0.444035 | 0.928347 | 0.314209 | 0.910683 | 0.901376 |
aq_prob_df = {}
for org, dat in aquatic_dat.items():
obs = cla_pt.transform([dat])
pred = classify_model.predict(obs)
pred_proba = classify_model.predict_proba(obs)
print(org, pred, pred_proba)
aq_prob_df[org] = pred_proba
('IV-2012_1.obs', array(['neutral'], dtype=object), array([[0.00696564, 0.09844835, 0.89458601]])) ('II-2015_1.obs', array(['neutral'], dtype=object), array([[0.02490085, 0.02888774, 0.94621141]])) ('IV-2015_1.obs', array(['neutral'], dtype=object), array([[0.004404 , 0.01150979, 0.98408621]])) ('V-2013_4.obs', array(['neutral'], dtype=object), array([[0.01512996, 0.12124855, 0.86362149]])) ('I-2008_7.obs', array(['neutral'], dtype=object), array([[0.0088035 , 0.11295349, 0.87824302]])) ('IV-2014_7.obs', array(['neutral'], dtype=object), array([[0.01647741, 0.01572721, 0.96779538]])) ('I-2012_1.obs', array(['neutral'], dtype=object), array([[0.01146352, 0.10870294, 0.87983354]])) ('I-2008_1.obs', array(['neutral'], dtype=object), array([[0.01424472, 0.10570858, 0.8800467 ]])) ('III-2015_1.obs', array(['neutral'], dtype=object), array([[0.01781934, 0.01943526, 0.96274541]])) ('II-2012_4.obs', array(['neutral'], dtype=object), array([[0.00656052, 0.10229448, 0.891145 ]])) ('II-2010_4.obs', array(['neutral'], dtype=object), array([[0.01572133, 0.04864126, 0.9356374 ]])) ('IV-2012_7.obs', array(['neutral'], dtype=object), array([[0.01376778, 0.11712759, 0.86910462]])) ('III-2011_7.obs', array(['neutral'], dtype=object), array([[0.01370599, 0.10907952, 0.87721449]])) ('I-2011_7.obs', array(['neutral'], dtype=object), array([[0.00650906, 0.11197057, 0.88152037]])) ('I-2009_1.obs', array(['neutral'], dtype=object), array([[0.0000e+00, 2.5000e-04, 9.9975e-01]])) ('V-2009_7.obs', array(['neutral'], dtype=object), array([[0.01343795, 0.06464551, 0.92191654]])) ('V-2010_1_1.obs', array(['neutral'], dtype=object), array([[0.0111527 , 0.11331063, 0.87553667]])) ('II-2010_7.obs', array(['neutral'], dtype=object), array([[0.02128433, 0.1096227 , 0.86909297]])) ('IV-2007_4.obs', array(['neutral'], dtype=object), array([[0.00535914, 0.02501437, 0.96962649]])) ('V-2007_7.obs', array(['neutral'], dtype=object), array([[0.01938502, 0.1062482 , 0.87436678]])) ('V-2011_7.obs', array(['neutral'], dtype=object), array([[0.02459286, 0.0989987 , 0.87640844]])) ('III-2007_1.obs', array(['neutral'], dtype=object), array([[0.00338683, 0.01189911, 0.98471406]])) ('III-2009_7.obs', array(['neutral'], dtype=object), array([[0.01017605, 0.04222821, 0.94759574]])) ('II-2012_7.obs', array(['neutral'], dtype=object), array([[0.00862686, 0.05112755, 0.94024559]])) ('II-2011_4.obs', array(['neutral'], dtype=object), array([[0.01916915, 0.10080323, 0.88002763]])) ('I-2014_4.obs', array(['neutral'], dtype=object), array([[0.01454291, 0.01711812, 0.96833898]])) ('III-2013_4.obs', array(['neutral'], dtype=object), array([[0.01793601, 0.08857434, 0.89348965]])) ('I-2007_7.obs', array(['neutral'], dtype=object), array([[0.01301471, 0.11174766, 0.87523763]])) ('III-2014_11.obs', array(['neutral'], dtype=object), array([[0.01202871, 0.04519664, 0.94277465]])) ('II-2008_7.obs', array(['neutral'], dtype=object), array([[0.01187798, 0.1176047 , 0.87051732]])) ('IV-2013_4.obs', array(['neutral'], dtype=object), array([[0.00949172, 0.09898623, 0.89152205]])) ('V-2009_4.obs', array(['neutral'], dtype=object), array([[0.00704663, 0.01388839, 0.97906498]])) ('II-2012_1_1.obs', array(['neutral'], dtype=object), array([[0.02105592, 0.12807237, 0.85087172]])) ('III-2014_4.obs', array(['neutral'], dtype=object), array([[0.00953875, 0.01403347, 0.97642778]])) ('IV-2013_1.obs', array(['neutral'], dtype=object), array([[0.01121068, 0.10691777, 0.88187155]])) ('V-2007_1.obs', array(['neutral'], dtype=object), array([[0.01598214, 0.11361523, 0.87040263]])) ('II-2007_7.obs', array(['neutral'], dtype=object), array([[0.02024397, 0.1169824 , 0.86277364]])) ('V-2008_7.obs', array(['neutral'], dtype=object), array([[0.01425529, 0.11167165, 0.87407306]])) ('III-2011_4.obs', array(['neutral'], dtype=object), array([[0.01565897, 0.10943575, 0.87490529]])) ('IV-2009_1_1.obs', array(['neutral'], dtype=object), array([[0.01512455, 0.10936483, 0.87551062]])) ('V-2014_11.obs', array(['neutral'], dtype=object), array([[0.01077124, 0.01451214, 0.97471663]])) ('V-2009_1_1.obs', array(['neutral'], dtype=object), array([[0.01468189, 0.11625975, 0.86905836]])) ('IV-2012_4.obs', array(['neutral'], dtype=object), array([[0.00607776, 0.05044154, 0.94348071]])) ('III-2012_4.obs', array(['neutral'], dtype=object), array([[0.06188391, 0.18059655, 0.75751953]])) ('II-2009_7.obs', array(['neutral'], dtype=object), array([[0.00372409, 0.03115693, 0.96511898]])) ('II-2010_1.obs', array(['neutral'], dtype=object), array([[0.01020062, 0.09458049, 0.89521889]])) ('I-2010_1_1.obs', array(['neutral'], dtype=object), array([[0.00740936, 0.11299984, 0.8795908 ]])) ('V-2015_1.obs', array(['neutral'], dtype=object), array([[0.01633852, 0.0168289 , 0.96683258]])) ('II-2011_7.obs', array(['neutral'], dtype=object), array([[0.01739962, 0.1106431 , 0.87195728]])) ('V-2011_4.obs', array(['neutral'], dtype=object), array([[0.01654235, 0.1267429 , 0.85671474]])) ('II-2010_1_1.obs', array(['neutral'], dtype=object), array([[0.01449134, 0.10832059, 0.87718807]])) ('IV-2009_7.obs', array(['neutral'], dtype=object), array([[0.01365381, 0.05186814, 0.93447805]])) ('V-2012_7.obs', array(['neutral'], dtype=object), array([[0.02428992, 0.12146583, 0.85424426]])) ('IV-2010_7.obs', array(['neutral'], dtype=object), array([[0.02362839, 0.11421454, 0.86215707]])) ('IV-2012_1_1.obs', array(['neutral'], dtype=object), array([[0.01755671, 0.1164891 , 0.86595419]])) ('II-2014_11.obs', array(['neutral'], dtype=object), array([[0.01001267, 0.01408856, 0.97589877]])) ('III-2009_4.obs', array(['neutral'], dtype=object), array([[0.01187824, 0.02667614, 0.96144562]])) ('I-2013_4.obs', array(['neutral'], dtype=object), array([[0.01747335, 0.10351738, 0.87900927]])) ('I-2011_1.obs', array(['neutral'], dtype=object), array([[0.01104239, 0.12950788, 0.85944973]])) ('I-2012_1_1.obs', array(['neutral'], dtype=object), array([[0.0127285, 0.1156301, 0.8716414]])) ('III-2009_1.obs', array(['neutral'], dtype=object), array([[0.00317316, 0.01219724, 0.9846296 ]])) ('I-2010_1.obs', array(['neutral'], dtype=object), array([[0.04830897, 0.17832924, 0.7733618 ]])) ('II-2012_1.obs', array(['neutral'], dtype=object), array([[0.00590415, 0.00927164, 0.98482421]])) ('V-2010_7.obs', array(['neutral'], dtype=object), array([[0.00944338, 0.12070322, 0.86985341]])) ('II-2013_4.obs', array(['neutral'], dtype=object), array([[0.01495139, 0.11373913, 0.87130948]])) ('III-2012_1_1.obs', array(['neutral'], dtype=object), array([[0.02934005, 0.11350875, 0.8571512 ]])) ('II-2009_4.obs', array(['neutral'], dtype=object), array([[0.01077805, 0.0236696 , 0.96555236]])) ('V-2012_4.obs', array(['neutral'], dtype=object), array([[0.02625816, 0.11968488, 0.85405695]])) ('IV-2007_1.obs', array(['neutral'], dtype=object), array([[0.02324862, 0.10929086, 0.86746053]])) ('V-2013_1.obs', array(['neutral'], dtype=object), array([[0.0221319, 0.1126806, 0.8651875]])) ('I-2007_4.obs', array(['neutral'], dtype=object), array([[0.01069246, 0.10882505, 0.88048249]])) ('V-2013_7.obs', array(['neutral'], dtype=object), array([[0.03494458, 0.12966587, 0.83538955]])) ('II-2013_7.obs', array(['neutral'], dtype=object), array([[0.02442412, 0.1132822 , 0.86229368]])) ('V-2010_4.obs', array(['neutral'], dtype=object), array([[0.00626976, 0.11972978, 0.87400046]])) ('III-2010_1_1.obs', array(['neutral'], dtype=object), array([[0.01708852, 0.13232931, 0.85058217]])) ('III-2011_1.obs', array(['neutral'], dtype=object), array([[0.01428337, 0.093707 , 0.89200963]])) ('III-2007_4.obs', array(['neutral'], dtype=object), array([[0.01941641, 0.11909626, 0.86148734]])) ('III-2014_7.obs', array(['neutral'], dtype=object), array([[0.01744637, 0.02747724, 0.95507639]])) ('V-2010_1.obs', array(['neutral'], dtype=object), array([[0.01920497, 0.08619869, 0.89459634]])) ('III-2009_1_1.obs', array(['neutral'], dtype=object), array([[0.02146081, 0.11578369, 0.8627555 ]])) ('I-2010_4.obs', array(['neutral'], dtype=object), array([[0.01460283, 0.1137277 , 0.87166947]])) ('III-2010_1.obs', array(['neutral'], dtype=object), array([[0.01313799, 0.10375949, 0.88310253]])) ('IV-2011_7.obs', array(['neutral'], dtype=object), array([[0.01045726, 0.11182998, 0.87771276]])) ('V-2012_1.obs', array(['neutral'], dtype=object), array([[0.02074468, 0.10837727, 0.87087805]])) ('III-2013_7.obs', array(['neutral'], dtype=object), array([[0.02415518, 0.11735868, 0.85848614]])) ('III-2012_1.obs', array(['neutral'], dtype=object), array([[0.00954951, 0.02878296, 0.96166752]])) ('V-2009_1.obs', array(['neutral'], dtype=object), array([[0.0102251 , 0.01751768, 0.97225722]])) ('V-2008_1.obs', array(['neutral'], dtype=object), array([[0.01583297, 0.11163724, 0.87252979]])) ('V-2007_4.obs', array(['neutral'], dtype=object), array([[0.01720743, 0.11119074, 0.87160183]])) ('IV-2010_4.obs', array(['neutral'], dtype=object), array([[0.01125051, 0.03334883, 0.95540066]])) ('I-2015_1.obs', array(['neutral'], dtype=object), array([[0.00257237, 0.00522353, 0.9922041 ]])) ('IV-2008_7.obs', array(['neutral'], dtype=object), array([[0.01741631, 0.11032456, 0.87225914]])) ('III-2008_1.obs', array(['neutral'], dtype=object), array([[0.01364963, 0.1102804 , 0.87606997]])) ('IV-2010_1.obs', array(['neutral'], dtype=object), array([[0.01541483, 0.1068171 , 0.87776807]])) ('I-2009_7.obs', array(['neutral'], dtype=object), array([[0.01283442, 0.06816585, 0.91899974]])) ('II-2014_4.obs', array(['neutral'], dtype=object), array([[0.01426931, 0.03158918, 0.9541415 ]])) ('IV-2014_4.obs', array(['neutral'], dtype=object), array([[0.01376531, 0.01789441, 0.96834028]])) ('II-2013_1.obs', array(['neutral'], dtype=object), array([[0.03542956, 0.13240116, 0.83216927]])) ('I-2007_1.obs', array(['neutral'], dtype=object), array([[0.02863442, 0.13495959, 0.83640599]])) ('V-2014_7.obs', array(['neutral'], dtype=object), array([[0.00942963, 0.01543426, 0.97513611]])) ('I-2010_7.obs', array(['neutral'], dtype=object), array([[0.01212671, 0.11346532, 0.87440797]])) ('II-2014_7.obs', array(['neutral'], dtype=object), array([[0.01493106, 0.02046522, 0.96460372]])) ('I-2012_4.obs', array(['neutral'], dtype=object), array([[0.01856125, 0.11377693, 0.86766182]])) ('III-2007_7.obs', array(['neutral'], dtype=object), array([[0.02335129, 0.10984889, 0.86679981]])) ('V-2011_1.obs', array(['neutral'], dtype=object), array([[0.03172073, 0.13511764, 0.83316162]])) ('I-2009_4.obs', array(['neutral'], dtype=object), array([[0.002625 , 0.00389583, 0.99347917]])) ('III-2010_7.obs', array(['neutral'], dtype=object), array([[0.02185331, 0.11062826, 0.86751843]])) ('I-2013_7.obs', array(['neutral'], dtype=object), array([[0.01623873, 0.1095071 , 0.87425418]])) ('IV-2009_1.obs', array(['neutral'], dtype=object), array([[0.00942176, 0.03925392, 0.95132431]])) ('IV-2008_1.obs', array(['neutral'], dtype=object), array([[0.02223076, 0.10591338, 0.87185586]])) ('II-2007_4.obs', array(['neutral'], dtype=object), array([[0.01060439, 0.1112803 , 0.87811531]])) ('IV-2009_4.obs', array(['neutral'], dtype=object), array([[0.00613958, 0.01596577, 0.97789465]])) ('II-2009_1.obs', array(['neutral'], dtype=object), array([[5.0000e-04, 7.5000e-04, 9.9875e-01]])) ('IV-2011_4.obs', array(['neutral'], dtype=object), array([[0.05364975, 0.15903777, 0.78731247]])) ('I-2011_4.obs', array(['neutral'], dtype=object), array([[0.02609871, 0.12301195, 0.85088934]])) ('III-2010_4.obs', array(['neutral'], dtype=object), array([[0.02338046, 0.11237694, 0.8642426 ]])) ('III-2012_7.obs', array(['neutral'], dtype=object), array([[0.00596645, 0.0439094 , 0.95012415]])) ('V-2012_1_1.obs', array(['neutral'], dtype=object), array([[0.05002316, 0.14119373, 0.8087831 ]])) ('I-2009_1_1.obs', array(['neutral'], dtype=object), array([[0.02747168, 0.12915164, 0.84337668]])) ('IV-2007_7.obs', array(['neutral'], dtype=object), array([[0.03418299, 0.12909611, 0.8367209 ]])) ('III-2008_7.obs', array(['neutral'], dtype=object), array([[0.02324862, 0.10929086, 0.86746053]])) ('II-2009_1_1.obs', array(['neutral'], dtype=object), array([[0.01449269, 0.09753805, 0.88796926]])) ('II-2008_1.obs', array(['neutral'], dtype=object), array([[0.01598248, 0.12106283, 0.86295469]])) ('II-2007_1.obs', array(['neutral'], dtype=object), array([[0.02360823, 0.10293125, 0.87346053]])) ('I-2014_11.obs', array(['neutral'], dtype=object), array([[0.00691892, 0.01424955, 0.97883153]])) ('IV-2014_11.obs', array(['neutral'], dtype=object), array([[0.01706721, 0.03022207, 0.95271072]])) ('I-2012_7.obs', array(['neutral'], dtype=object), array([[0.01715923, 0.11141968, 0.87142109]])) ('IV-2010_1_1.obs', array(['neutral'], dtype=object), array([[0.01231521, 0.09754963, 0.89013517]])) ('II-2011_1.obs', array(['neutral'], dtype=object), array([[0.02630231, 0.12252029, 0.85117741]])) ('IV-2011_1.obs', array(['neutral'], dtype=object), array([[0.00706561, 0.07312341, 0.91981097]])) ('I-2013_1.obs', array(['neutral'], dtype=object), array([[0.02336751, 0.11620656, 0.86042592]])) ('IV-2013_7.obs', array(['neutral'], dtype=object), array([[0.01861135, 0.11695142, 0.86443724]])) ('I-2014_7.obs', array(['neutral'], dtype=object), array([[0.01440018, 0.02119765, 0.96440216]])) ('V-2014_4.obs', array(['neutral'], dtype=object), array([[0.01066641, 0.01644229, 0.9728913 ]]))
colors = ["red", "orange","blue"]
aq_proba_df = {site:dat[0] for site, dat in aq_prob_df.items()}
aq_proba_df = pd.DataFrame.from_dict(aq_proba_df, orient="index", columns=["competition", "filtering", "neutral"]).T
#display(aq_proba_df)
for siteid in ["I", "II", "III", "IV", "V"]:
site = aq_proba_df.filter(regex="^{}-.*.obs$".format(siteid))
site = site.reindex(sorted(site.columns), axis=1)
site.columns = [x.split(".")[0].split("-")[1] for x in site.columns]
ax = site.T.loc[:,["competition", "filtering", "neutral"]].plot.bar(stacked=True, color=colors, figsize=(15,3), fontsize=15, legend=False)
ax.set_title("Site {}".format(siteid), fontsize=20)
from sklearn.model_selection import validation_curve
degree = np.arange(1, 10)
transform = True
#transform = False
if transform:
pt = PowerTransformer()
#pt = StandardScaler()
#pt = QuantileTransformer()
Xtrans = pt.fit_transform(X)
train_score, val_score = validation_curve(RandomForestRegressor(), Xtrans, y,
'max_depth', degree, cv=7)
plt.plot(degree, np.median(train_score, 1), color='blue', label='training score')
plt.plot(degree, np.median(val_score, 1), color='red', label='validation score')
plt.legend(loc='best')
plt.ylim(0, 1)
plt.xlabel('degree')
plt.ylabel('score');
## This is why pipelines are useful
from sklearn.pipeline import make_pipeline
pipe = make_pipeline(StandardScaler(), RandomForestRegressor())
pipe.fit(Xtrain, ytrain)
pipe.score(Xtest, ytest)
ValueErrorTraceback (most recent call last) <ipython-input-531-d9a93c616f08> in <module>() 2 from sklearn.pipeline import make_pipeline 3 pipe = make_pipeline(StandardScaler(), RandomForestRegressor()) ----> 4 pipe.fit(Xtrain, ytrain) 5 pipe.score(Xtest, ytest) /home/isaac/miniconda2/envs/ipyrad/lib/python2.7/site-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params) 265 Xt, fit_params = self._fit(X, y, **fit_params) 266 if self._final_estimator is not None: --> 267 self._final_estimator.fit(Xt, y, **fit_params) 268 return self 269 /home/isaac/miniconda2/envs/ipyrad/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc in fit(self, X, y, sample_weight) 277 278 if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous: --> 279 y = np.ascontiguousarray(y, dtype=DOUBLE) 280 281 if expanded_class_weight is not None: /home/isaac/miniconda2/envs/ipyrad/lib/python2.7/site-packages/numpy/core/numeric.pyc in ascontiguousarray(a, dtype) 588 589 """ --> 590 return array(a, dtype, copy=False, order='C', ndmin=1) 591 592 ValueError: could not convert string to float: filtering
X, y = get_features_targets(full_df, targets=["model"])
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)
n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap}
rf_random = RandomizedSearchCV(estimator = RandomForestClassifier(),\
param_distributions = random_grid,
n_iter = 100, cv = 3, verbose=2)#, n_jobs = -1)
rf_random.fit(Xtrain, ytrain)
Fitting 3 folds for each of 100 candidates, totalling 300 fits [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=90
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=90, total= 10.3s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=90
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 11.1s remaining: 0.0s
[CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=90, total= 10.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=90 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=90, total= 9.8s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80, total= 5.4s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80, total= 5.3s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=80, total= 5.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=110 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=110, total= 4.8s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=110 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=110, total= 4.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=110 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=110, total= 5.0s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=90, total= 9.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=90, total= 8.9s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=90, total= 9.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=40, total= 3.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=40, total= 3.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=40, total= 3.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=80, total= 4.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=80, total= 4.5s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=80, total= 4.7s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=50, total= 8.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=50, total= 7.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=10, max_depth=50, total= 8.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80, total= 3.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80, total= 3.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=80, total= 3.1s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=110, total= 10.9s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=110, total= 10.8s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=10, max_depth=110, total= 10.9s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=5, max_depth=20 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=5, max_depth=20, total= 6.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=5, max_depth=20 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=5, max_depth=20, total= 6.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=5, max_depth=20 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=5, max_depth=20, total= 6.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=20, total= 16.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=20, total= 16.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=2, max_depth=20, total= 16.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=80, total= 3.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=80, total= 3.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=80, total= 3.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None, total= 3.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None, total= 3.4s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=None, total= 3.3s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=30, total= 4.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=30, total= 4.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=auto, min_samples_split=10, max_depth=30, total= 4.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=50, total= 10.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=50, total= 10.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=50, total= 10.6s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=10, total= 9.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=10, total= 8.6s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=5, max_depth=10, total= 8.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=100, total= 9.3s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=100, total= 9.0s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=5, max_depth=100, total= 9.4s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=110 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=110, total= 4.9s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=110 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=110, total= 4.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=110 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=110, total= 4.8s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=90, total= 12.8s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=90, total= 12.4s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=90, total= 12.5s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=30, total= 6.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=30, total= 6.7s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=30, total= 6.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=90 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=90, total= 7.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=90 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=90, total= 7.8s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=90 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=90, total= 7.6s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=80, total= 2.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=80, total= 2.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=80, total= 2.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60, total= 9.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60, total= 9.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60, total= 9.4s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=30, total= 11.1s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=30, total= 11.1s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=30, total= 11.0s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=40 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=40, total= 8.6s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=40 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=40, total= 8.4s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=40 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=40, total= 8.4s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=None, total= 5.5s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=None, total= 5.5s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=10, max_depth=None, total= 5.5s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=10, total= 3.8s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=10, total= 4.0s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=10, total= 3.8s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=60, total= 5.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=60, total= 5.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=60, total= 5.4s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=100, total= 3.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=100, total= 3.5s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=100, total= 3.5s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=50 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=50, total= 4.7s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=50 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=50, total= 4.6s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=50 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=50, total= 4.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30, total= 8.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30, total= 8.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=auto, min_samples_split=5, max_depth=30, total= 8.2s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=100, total= 14.2s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=100, total= 13.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1800, max_features=auto, min_samples_split=10, max_depth=100, total= 13.4s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10, total= 9.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10, total= 8.9s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=10, total= 9.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=60, total= 8.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=60, total= 8.0s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=60, total= 8.0s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total= 5.9s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total= 5.8s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total= 5.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=20, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=20, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=20, total= 1.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=2, max_depth=70 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=2, max_depth=70, total= 13.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=2, max_depth=70 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=2, max_depth=70, total= 12.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=2, max_depth=70 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=2, max_depth=70, total= 13.1s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=20 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=20, total= 4.6s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=20 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=20, total= 4.5s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=20 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=5, max_depth=20, total= 4.5s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=None, total= 3.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=None, total= 3.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=600, max_features=auto, min_samples_split=2, max_depth=None, total= 3.6s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=100, total= 2.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=100, total= 2.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=2, max_depth=100, total= 2.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=10 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=10, total= 13.0s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=10 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=10, total= 12.9s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=10 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=10, total= 13.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=50, total= 4.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=50, total= 4.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=50, total= 4.5s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=30, total= 3.0s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=30, total= 2.8s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=30, total= 2.8s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=60, total= 4.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=60, total= 4.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=60, total= 4.2s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=30, total= 1.4s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=30, total= 1.5s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=30, total= 1.4s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None, total= 1.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None, total= 1.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None, total= 1.0s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=50, total= 2.0s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=50, total= 2.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=sqrt, min_samples_split=10, max_depth=50, total= 2.1s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100, total= 11.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100, total= 10.9s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=2000, max_features=auto, min_samples_split=10, max_depth=100, total= 10.9s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=20, total= 7.9s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=20, total= 7.7s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1000, max_features=auto, min_samples_split=2, max_depth=20, total= 7.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10, total= 8.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10, total= 8.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=5, max_depth=10, total= 8.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=None, total= 4.9s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=None, total= 4.8s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=None, total= 4.8s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=20, total= 11.0s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=20, total= 11.1s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=2, max_depth=20, total= 11.0s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100, total= 4.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100, total= 4.4s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=2, max_depth=100, total= 4.3s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total= 4.5s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total= 4.4s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=800, max_features=sqrt, min_samples_split=10, max_depth=30, total= 4.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60, total= 6.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60, total= 6.4s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=10, max_depth=60, total= 6.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=10, total= 6.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=10, total= 6.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=10, total= 6.5s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=80, total= 9.9s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=80, total= 9.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=80 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=80, total= 9.7s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=60 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=60, total= 2.3s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=60 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=60, total= 2.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=60 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=60, total= 2.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90, total= 11.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90, total= 11.3s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90, total= 11.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=40 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=40, total= 14.8s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=40 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=40, total= 14.5s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=40 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=40, total= 14.4s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=70 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=70, total= 8.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=70 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=70, total= 7.9s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=70 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=5, max_depth=70, total= 7.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=100, total= 8.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=100, total= 8.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=100 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=5, max_depth=100, total= 8.0s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60, total= 3.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60, total= 3.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=60, total= 3.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=110, total= 8.9s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=110, total= 8.8s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=110, total= 8.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=50, total= 8.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=50, total= 8.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=50 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=10, max_depth=50, total= 8.3s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=100, total= 9.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=100, total= 9.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=100, total= 9.4s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=50, total= 5.8s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=50, total= 5.7s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=50 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1000, max_features=sqrt, min_samples_split=5, max_depth=50, total= 5.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None, total= 4.3s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None, total= 4.2s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=800, max_features=auto, min_samples_split=5, max_depth=None, total= 4.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None, total= 1.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None, total= 1.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None [CV] bootstrap=False, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=None, total= 1.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=110, total= 2.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=110, total= 2.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=110, total= 2.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=110, total= 1.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=110, total= 1.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=200, max_features=sqrt, min_samples_split=10, max_depth=110, total= 1.1s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=80 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=80, total= 3.4s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=80 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=80, total= 3.4s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=80 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=2, max_depth=80, total= 3.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=None, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=None, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=None [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=None, total= 1.2s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=100, total= 1.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=100, total= 1.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=2, max_depth=100, total= 1.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=60 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=60, total= 10.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=60 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=60, total= 10.3s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=60 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=60, total= 10.5s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=20 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=20, total= 13.1s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=20 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=20, total= 12.8s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=20 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1800, max_features=sqrt, min_samples_split=10, max_depth=20, total= 13.1s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=20 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=20, total= 4.4s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=20 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=20, total= 4.4s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=20 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=600, max_features=auto, min_samples_split=5, max_depth=20, total= 4.4s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=100, total= 12.7s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=100, total= 12.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=100, total= 12.4s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=60, total= 11.4s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=60, total= 11.4s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=sqrt, min_samples_split=2, max_depth=60, total= 11.2s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=10, total= 2.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=10, total= 2.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=10 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=5, max_depth=10, total= 2.6s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=None, total= 8.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=None, total= 8.7s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=None [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=None, total= 8.5s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=20 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=20, total= 9.3s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=20 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=20, total= 9.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=20 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1200, max_features=auto, min_samples_split=10, max_depth=20, total= 9.1s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=110 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=110, total= 4.7s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=110 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=110, total= 4.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=110 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=600, max_features=sqrt, min_samples_split=5, max_depth=110, total= 4.6s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=50 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=50, total= 2.3s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=50 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=50, total= 2.3s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=50 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=2, max_depth=50, total= 2.3s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=10, total= 7.6s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=10, total= 7.5s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=sqrt, min_samples_split=2, max_depth=10, total= 7.6s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=90 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=90, total= 8.9s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=90 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=90, total= 8.9s [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=90 [CV] bootstrap=False, min_samples_leaf=4, n_estimators=1200, max_features=auto, min_samples_split=2, max_depth=90, total= 8.9s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=40, total= 9.5s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=40, total= 9.6s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=sqrt, min_samples_split=2, max_depth=40, total= 9.5s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=100, total= 2.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=100, total= 2.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=100 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=400, max_features=auto, min_samples_split=10, max_depth=100, total= 2.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=40 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=40, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=40 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=40, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=40 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=200, max_features=sqrt, min_samples_split=5, max_depth=40, total= 1.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90, total= 16.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90, total= 16.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=90, total= 16.1s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=20, total= 15.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=20, total= 15.2s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=20 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=5, max_depth=20, total= 15.5s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=60, total= 3.2s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=60, total= 3.1s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=60 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=400, max_features=sqrt, min_samples_split=5, max_depth=60, total= 3.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=110, total= 9.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=110, total= 9.5s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=110 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1800, max_features=auto, min_samples_split=5, max_depth=110, total= 9.4s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=30, total= 1.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=30, total= 1.6s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=30 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=200, max_features=auto, min_samples_split=5, max_depth=30, total= 1.5s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=10, total= 13.2s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=10, total= 12.8s [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=10 [CV] bootstrap=False, min_samples_leaf=2, n_estimators=2000, max_features=sqrt, min_samples_split=2, max_depth=10, total= 12.8s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=40, total= 1.2s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=40, total= 1.1s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=40 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=200, max_features=sqrt, min_samples_split=2, max_depth=40, total= 1.2s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=80, total= 9.0s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=80, total= 8.9s [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=80 [CV] bootstrap=True, min_samples_leaf=1, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=80, total= 9.1s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=20 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=20, total= 8.4s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=20 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=20, total= 8.4s [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=20 [CV] bootstrap=True, min_samples_leaf=4, n_estimators=1600, max_features=auto, min_samples_split=10, max_depth=20, total= 8.5s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=100, total= 5.7s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=100, total= 5.6s [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=100 [CV] bootstrap=True, min_samples_leaf=2, n_estimators=1000, max_features=sqrt, min_samples_split=2, max_depth=100, total= 5.7s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=40 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=40, total= 11.0s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=40 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=40, total= 10.6s [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=40 [CV] bootstrap=False, min_samples_leaf=1, n_estimators=1400, max_features=auto, min_samples_split=10, max_depth=40, total= 10.7s
[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 36.4min finished
RandomizedSearchCV(cv=3, error_score='raise-deprecating', estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None, oob_score=False, random_state=None, verbose=0, warm_start=False), fit_params=None, iid='warn', n_iter=100, n_jobs=None, param_distributions={'bootstrap': [True, False], 'min_samples_leaf': [1, 2, 4], 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'min_samples_split': [2, 5, 10], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None]}, pre_dispatch='2*n_jobs', random_state=None, refit=True, return_train_score='warn', scoring=None, verbose=2)
rf_random.best_params_
{'bootstrap': True, 'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 800}
## The most impo
scores = ['precision', 'recall']
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
clf = GridSearchCV(RandomForestClassifier(), tuned_parameters, cv=5)
clf.fit(X_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
print()
print("Grid scores on development set:")
print()
means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
print()
print("Detailed classification report:")
print()
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
print()
y_true, y_pred = y_test, clf.predict(X_test)
print(classification_report(y_true, y_pred))
print()
classifier = RandomForestClassifier(max_depth=2, n_estimators=100)
classifier.fit(X, y)
print(regr.feature_importances_)
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)
transform = True
if transform:
pt = PowerTransformer()
#pt = StandardScaler()
#pt = QuantileTransformer()
Xtrain = pt.fit_transform(Xtrain)
Xtest = pt.fit_transform(Xtest)
model = RandomForestClassifier(n_estimators=100, n_jobs=-1, max_depth=7)
model.fit(Xtrain, ytrain)
ypred = model.predict(Xtest)
print(model.feature_importances_)
cm = metrics.confusion_matrix(ypred, ytest)
print(metrics.classification_report(ypred, ytest))
plot_confusion_matrix(cm, ["filtering", "neutral"])
import glob
aquatic_regress_dat
aquatic_df = pd.DataFrame.from_dict(aquatic_regress_dat, orient="index").T
display(aquatic_df)
for site in ["I", "II", "III", "IV", "V"]:
site = aquatic_df.filter(regex="^{}-2008".format(site))
h = sorted(site.columns)
#print(site[h])
for yr in [2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015]:
for mo in [1, 4, 7]:
site = aquatic_df.filter(regex="^III-{}_{}.obs$".format(yr, mo))
print(site.values),
print()
IV-2012_1.obs | II-2015_1.obs | IV-2015_1.obs | V-2013_4.obs | I-2008_7.obs | IV-2014_7.obs | IV-2009_4.obs | I-2008_1.obs | III-2015_1.obs | II-2012_4.obs | ... | I-2014_11.obs | IV-2014_11.obs | V-2014_4.obs | IV-2010_1_1.obs | III-2013_7.obs | IV-2011_1.obs | I-2013_1.obs | IV-2013_7.obs | I-2014_7.obs | I-2012_7.obs | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.442475 | 0.446899 | 0.454998 | 0.462331 | 0.453011 | 0.457588 | 0.454498 | 0.460543 | 0.455845 | 0.44476 | ... | 0.448516 | 0.45678 | 0.461633 | 0.452911 | 0.444115 | 0.446079 | 0.436161 | 0.449765 | 0.456283 | 0.452861 |
1 rows × 134 columns
[[0.44697102]] [] [[0.47436522]] () [[0.45628585]] [[0.44193741]] [[0.46574151]] () [[0.45345792]] [[0.45380692]] [[0.46751038]] () [[0.44489159]] [[0.45127214]] [[0.45512112]] () [[0.45152444]] [[0.44888263]] [[0.45425377]] () [] [[0.43964601]] [[0.44411455]] () [] [[0.45740211]] [[0.4537343]] () [[0.45584548]] [] [] ()
X[["mn_local_traits", "var_local_traits"]] = neut_df[["mn_local_traits", "var_local_traits"]]
X
X.drop(["SGD_0"], axis=1)
SGD_1 | SGD_2 | SGD_3 | SGD_4 | SGD_5 | SGD_6 | SGD_7 | SGD_8 | SGD_9 | var_local_traits | mn_local_traits | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.232143 | 0.116071 | 0.089286 | 0.017857 | 0.026786 | 0.017857 | 0.017857 | 0.000000 | 0.008929 | 24.874761 | 0.287353 |
1 | 0.196721 | 0.073770 | 0.049180 | 0.040984 | 0.016393 | 0.000000 | 0.000000 | 0.008197 | 0.008197 | 11.409320 | -0.087190 |
2 | 0.196581 | 0.094017 | 0.094017 | 0.034188 | 0.008547 | 0.034188 | 0.017094 | 0.000000 | 0.008547 | 20.606043 | 2.265735 |
3 | 0.184874 | 0.109244 | 0.042017 | 0.008403 | 0.008403 | 0.000000 | 0.008403 | 0.000000 | 0.008403 | 20.587360 | -1.257110 |
4 | 0.202247 | 0.146067 | 0.033708 | 0.044944 | 0.022472 | 0.022472 | 0.011236 | 0.011236 | 0.022472 | 16.306985 | -4.042226 |
5 | 0.234848 | 0.128788 | 0.037879 | 0.015152 | 0.037879 | 0.037879 | 0.045455 | 0.007576 | 0.007576 | 19.283144 | 0.001284 |
6 | 0.208333 | 0.118056 | 0.048611 | 0.034722 | 0.027778 | 0.006944 | 0.000000 | 0.013889 | 0.006944 | 19.449519 | 0.206811 |
7 | 0.247619 | 0.066667 | 0.019048 | 0.019048 | 0.028571 | 0.000000 | 0.009524 | 0.000000 | 0.009524 | 26.959500 | -3.551698 |
8 | 0.221311 | 0.073770 | 0.065574 | 0.024590 | 0.016393 | 0.016393 | 0.000000 | 0.008197 | 0.008197 | 26.833052 | -5.289398 |
9 | 0.163462 | 0.057692 | 0.009615 | 0.009615 | 0.000000 | 0.009615 | 0.000000 | 0.009615 | 0.009615 | 18.737502 | -0.252489 |
10 | 0.225490 | 0.107843 | 0.029412 | 0.009804 | 0.009804 | 0.000000 | 0.019608 | 0.000000 | 0.019608 | 24.956961 | -1.464978 |
11 | 0.228346 | 0.078740 | 0.133858 | 0.047244 | 0.031496 | 0.023622 | 0.031496 | 0.007874 | 0.007874 | 15.212772 | 0.993274 |
12 | 0.142857 | 0.112782 | 0.082707 | 0.052632 | 0.007519 | 0.030075 | 0.015038 | 0.030075 | 0.007519 | 36.335040 | 3.918478 |
13 | 0.271186 | 0.101695 | 0.050847 | 0.016949 | 0.016949 | 0.000000 | 0.016949 | 0.000000 | 0.016949 | 26.248840 | -1.092308 |
14 | 0.201754 | 0.105263 | 0.052632 | 0.061404 | 0.035088 | 0.008772 | 0.017544 | 0.008772 | 0.017544 | 32.913075 | -0.690978 |
15 | 0.134921 | 0.134921 | 0.063492 | 0.063492 | 0.007937 | 0.007937 | 0.000000 | 0.000000 | 0.007937 | 16.476683 | -0.081903 |
16 | 0.187500 | 0.125000 | 0.043750 | 0.031250 | 0.018750 | 0.012500 | 0.000000 | 0.000000 | 0.006250 | 26.454639 | -1.896264 |
17 | 0.205479 | 0.089041 | 0.061644 | 0.041096 | 0.034247 | 0.006849 | 0.000000 | 0.000000 | 0.013699 | 13.869256 | 0.150719 |
18 | 0.225000 | 0.100000 | 0.091667 | 0.033333 | 0.000000 | 0.033333 | 0.025000 | 0.000000 | 0.008333 | 22.248610 | -0.952053 |
19 | 0.241071 | 0.098214 | 0.017857 | 0.062500 | 0.008929 | 0.000000 | 0.000000 | 0.000000 | 0.008929 | 16.667115 | 1.112054 |
20 | 0.268293 | 0.105691 | 0.081301 | 0.024390 | 0.032520 | 0.008130 | 0.016260 | 0.008130 | 0.008130 | 22.422884 | 3.576883 |
21 | 0.190476 | 0.115646 | 0.081633 | 0.027211 | 0.020408 | 0.006803 | 0.000000 | 0.013605 | 0.013605 | 19.807123 | 1.277888 |
22 | 0.153846 | 0.128205 | 0.119658 | 0.059829 | 0.034188 | 0.025641 | 0.008547 | 0.000000 | 0.025641 | 28.301400 | 2.105059 |
23 | 0.181034 | 0.137931 | 0.060345 | 0.043103 | 0.034483 | 0.008621 | 0.000000 | 0.008621 | 0.008621 | 32.591679 | -0.410971 |
24 | 0.137097 | 0.056452 | 0.032258 | 0.016129 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.008065 | 35.829194 | -1.833548 |
25 | 0.130769 | 0.076923 | 0.015385 | 0.007692 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.007692 | 28.690065 | 2.770645 |
26 | 0.186441 | 0.144068 | 0.093220 | 0.110169 | 0.033898 | 0.016949 | 0.033898 | 0.008475 | 0.033898 | 15.729761 | -0.384403 |
27 | 0.187500 | 0.083333 | 0.020833 | 0.031250 | 0.010417 | 0.020833 | 0.010417 | 0.000000 | 0.010417 | 17.273221 | -1.715901 |
28 | 0.055556 | 0.055556 | 0.166667 | 0.055556 | 0.055556 | 0.000000 | 0.055556 | 0.111111 | 0.055556 | 38.373334 | -2.796246 |
29 | 0.194631 | 0.026846 | 0.040268 | 0.006711 | 0.006711 | 0.000000 | 0.006711 | 0.000000 | 0.006711 | 22.558457 | 2.149222 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
1963 | 0.200000 | 0.145455 | 0.072727 | 0.036364 | 0.090909 | 0.018182 | 0.000000 | 0.000000 | 0.018182 | 20.466668 | 0.070724 |
1964 | 0.066667 | 0.044444 | 0.022222 | 0.000000 | 0.000000 | 0.022222 | 0.000000 | 0.000000 | 0.022222 | 23.371532 | -1.246761 |
1965 | 0.126761 | 0.070423 | 0.070423 | 0.000000 | 0.014085 | 0.014085 | 0.014085 | 0.000000 | 0.014085 | 21.034751 | 1.976945 |
1966 | 0.132530 | 0.048193 | 0.048193 | 0.048193 | 0.024096 | 0.000000 | 0.012048 | 0.000000 | 0.012048 | 19.422057 | -1.380979 |
1967 | 0.166667 | 0.074074 | 0.018519 | 0.000000 | 0.000000 | 0.000000 | 0.018519 | 0.000000 | 0.037037 | 21.501086 | 1.493715 |
1968 | 0.123077 | 0.076923 | 0.046154 | 0.061538 | 0.015385 | 0.015385 | 0.015385 | 0.000000 | 0.015385 | 17.776699 | -2.023371 |
1969 | 0.309091 | 0.090909 | 0.072727 | 0.054545 | 0.000000 | 0.000000 | 0.000000 | 0.018182 | 0.018182 | 21.701422 | 2.279736 |
1970 | 0.155172 | 0.034483 | 0.034483 | 0.017241 | 0.000000 | 0.000000 | 0.000000 | 0.017241 | 0.017241 | 20.661038 | -1.539468 |
1971 | 0.114754 | 0.032787 | 0.081967 | 0.032787 | 0.000000 | 0.016393 | 0.000000 | 0.016393 | 0.016393 | 23.933818 | -3.968882 |
1972 | 0.116667 | 0.050000 | 0.033333 | 0.016667 | 0.000000 | 0.016667 | 0.000000 | 0.016667 | 0.016667 | 20.805256 | 2.161174 |
1973 | 0.108108 | 0.189189 | 0.067568 | 0.067568 | 0.013514 | 0.040541 | 0.000000 | 0.000000 | 0.013514 | 16.022015 | 1.882169 |
1974 | 0.068182 | 0.000000 | 0.090909 | 0.022727 | 0.068182 | 0.045455 | 0.045455 | 0.000000 | 0.045455 | 17.072060 | 0.163465 |
1975 | 0.133333 | 0.088889 | 0.000000 | 0.000000 | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.022222 | 14.419553 | 0.538711 |
1976 | 0.146667 | 0.133333 | 0.040000 | 0.053333 | 0.013333 | 0.000000 | 0.013333 | 0.000000 | 0.013333 | 15.260348 | -2.653829 |
1977 | 0.224138 | 0.068966 | 0.068966 | 0.051724 | 0.017241 | 0.034483 | 0.051724 | 0.034483 | 0.051724 | 13.142348 | 1.270407 |
1978 | 0.230769 | 0.076923 | 0.057692 | 0.038462 | 0.000000 | 0.038462 | 0.000000 | 0.000000 | 0.038462 | 34.500092 | -5.048686 |
1979 | 0.150685 | 0.095890 | 0.041096 | 0.054795 | 0.000000 | 0.000000 | 0.027397 | 0.000000 | 0.027397 | 19.546045 | 0.393422 |
1980 | 0.150000 | 0.187500 | 0.050000 | 0.062500 | 0.037500 | 0.025000 | 0.000000 | 0.012500 | 0.012500 | 30.929675 | 0.610800 |
1981 | 0.149254 | 0.089552 | 0.089552 | 0.044776 | 0.014925 | 0.059701 | 0.000000 | 0.014925 | 0.014925 | 17.589162 | -0.188984 |
1982 | 0.104167 | 0.020833 | 0.062500 | 0.062500 | 0.041667 | 0.000000 | 0.020833 | 0.000000 | 0.020833 | 25.359490 | 2.000399 |
1983 | 0.137931 | 0.000000 | 0.017241 | 0.034483 | 0.034483 | 0.000000 | 0.000000 | 0.000000 | 0.034483 | 30.698115 | 2.174900 |
1984 | 0.253333 | 0.093333 | 0.013333 | 0.013333 | 0.026667 | 0.013333 | 0.013333 | 0.013333 | 0.026667 | 22.999522 | -0.130210 |
1985 | 0.076923 | 0.046154 | 0.046154 | 0.015385 | 0.046154 | 0.000000 | 0.000000 | 0.000000 | 0.015385 | 34.589831 | 0.646790 |
1986 | 0.179104 | 0.074627 | 0.059701 | 0.059701 | 0.014925 | 0.000000 | 0.000000 | 0.000000 | 0.014925 | 18.794791 | 0.781450 |
1987 | 0.069767 | 0.139535 | 0.046512 | 0.046512 | 0.023256 | 0.000000 | 0.023256 | 0.069767 | 0.023256 | 27.986888 | -0.554088 |
1988 | 0.215190 | 0.139241 | 0.101266 | 0.050633 | 0.037975 | 0.012658 | 0.000000 | 0.012658 | 0.012658 | 26.567355 | 1.341781 |
1989 | 0.180328 | 0.098361 | 0.049180 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.016393 | 24.162866 | 0.146806 |
1990 | 0.169811 | 0.018868 | 0.018868 | 0.018868 | 0.000000 | 0.000000 | 0.018868 | 0.000000 | 0.018868 | 19.325613 | -3.415909 |
1991 | 0.142857 | 0.061224 | 0.061224 | 0.040816 | 0.000000 | 0.000000 | 0.020408 | 0.000000 | 0.040816 | 19.946093 | -2.453620 |
1992 | 0.200000 | 0.013333 | 0.026667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.013333 | 36.025443 | -3.942698 |
5987 rows × 11 columns