#!/usr/bin/env python
# coding: utf-8

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# # Simple Constraints - Examples and Usage

# [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/basic/Constraints_Suite.ipynb)

# > This is a `whylogs v1` example. For the analog feature in `v0`, please refer to [this example](https://github.com/whylabs/whylogs/blob/maintenance/0.7.x/examples/Constraints_Suite.ipynb)

# In this example, we'll show how to define a number of simple constraints and examples on how to use them. For the basics on how to build your own set of constraints, see the example - [Data Validation with Metric Constraints](https://whylogs.readthedocs.io/en/stable/examples/advanced/Metric_Constraints.html).
# 
# The constraints are listed according to the metric namespace used when defining them. For each category, we will create helper functions for simple and popular constraints. Each helper function has a brief explanation in its docstring. After defining the helper functions, we'll show a simple example on how to build the constraints out of the functions and visualize them as a report with the visualization module.

# > Note: The constraints shown here are still experimental and subject to further changes. Stay tuned for upgrades!

# ## Completeness Constraints
# 
# | constraint                   | parameters          | semantic                                              | metric |
# |------------------------------|---------------------|-------------------------------------------------------|--------|
# | no_missing_values            | column name         | Checks that are no missing values in the column       | Counts |
# | null_values_below_number     | column name, number | Number of null values must be below given number.     | Counts |
# | null_percentage_below_number | column name, number | Percentage of null values must be below given number. | Counts |

# ## Consistency Constraints
# 
# | constraint                        | parameters                 | semantic                                                                               | metric         |
# |-----------------------------------|----------------------------|----------------------------------------------------------------------------------------|----------------|
# | greater_than_number               | column name                | Minimum value of given column must be above defined number.                            | Distribution   |
# | smaller_than_number               | column name, number        | Maximum value of given column must be below defined number.                            | Distribution   |
# | is_in_range                       | column name, lower, upper  | Checks that all of column's values are in defined range (inclusive).                   | Distribution   |
# | is_non_negative                   | column name                | Checks if a column is non negative.                                                    | Distribution   |
# | n_most_common_items_in_set        | column name, reference set | Checks if the top n most common items appear in the dataset.                           | Frequent Items |
# | frequent_strings_in_reference_set | column name, reference set | Checks if a set of variables appear in the frequent strings for a string column.       | Frequent Items |
# | count_below_number                | column name, number        | Checks if elements in a column are below given number.                                 | Counts         |
# | distinct_number_in_range          | column name, lower, upper  | Checks if number of distinct categories is between lower and upper values (inclusive). | Cardinality    |
# | column_is_nullable_integral       | column name                | Check if column contains only records of specific datatype.                            | Types          |
# | column_is_nullable_boolean        | column name                | Check if column contains only records of specific datatype.                            | Types          |
# | column_is_nullable_fractional     | column name                | Check if column contains only records of specific datatype.                            | Types          |
# | column_is_nullable_object         | column name                | Check if column contains only records of specific datatype.                            | Types          |
# | column_is_nullable_string         | column name                | Check if column contains only records of specific datatype.                            | Types          |

# ## Condition Count Constraints
# 
# Please refer to the example [Metric Constraints with Condition Count Metrics](https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Metric_Constraints_with_Condition_Count_Metrics.ipynb) for examples on how to use these constraints.
# 
# | constraint             | parameters                          | semantic                                                                             | metric       |
# |------------------------|-------------------------------------|--------------------------------------------------------------------------------------|--------------|
# | condition_meets     | column name, condition_name           | Fails if condition not met at least once.                        | Condition Count |
# | condition_never_meets   | column name, condition_name           | Fails if condition is met at least once          | Condition Count |
# | condition_count_below | column name, condition_name, max_count | Fails if condition is met more than max count | Condition Count |
# 
# ## Statistics Constraints
# 
# | constraint             | parameters                          | semantic                                                                             | metric       |
# |------------------------|-------------------------------------|--------------------------------------------------------------------------------------|--------------|
# | mean_between_range     | column name, lower, upper           | Mean must be between range defined by lower and upper bounds.                        | Distribution |
# | stddev_between_range   | column name, lower, upper           | Standard deviarion must be between range defined by lower and upper bounds.          | Distribution |
# | quantile_between_range | column name, quantile, lower, upper | Q-th quantile value must be withing the range defined by lower and upper boundaries. | Distribution |

# ## Table of Contents
# 
# - [Installing and Importing Modules](#pre)
# - [Distribution Metrics Constraints](#distribution)
# - [Frequent Items/Frequent Strings Metrics Constraints](#frequent)
# - [Counters Constraints](#counts)
# - [Cardinality Constraints](#card)
# - [Types Constraints](#types)
# - [Combined Metrics Constraints](#comb)
# 

# ## Installing whylogs and importing modules <a class="anchor" id="pre"></a>
# 
# If you haven't already, install whylogs:

# In[1]:


# Note: you may need to restart the kernel to use updated packages.
get_ipython().run_line_magic('pip', "install 'whylogs[viz]'")


# Then, let's import the helper functions needed to define the constraints:

# In[2]:


from whylogs.core.constraints import ConstraintsBuilder
from whylogs.core.constraints.factories import (
    greater_than_number,
    is_in_range,
    is_non_negative,
    mean_between_range,
    smaller_than_number,
    stddev_between_range,
    quantile_between_range
)


# ### Examples - Distribution Metrics Constraints

# In[3]:


import whylogs as why
import pandas as pd
data = {
    "animal": ["cat", "hawk", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, 4, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
}

results = why.log(pd.DataFrame(data))
profile_view = results.view()


# In[4]:


builder = ConstraintsBuilder(dataset_profile_view=profile_view)
builder.add_constraint(greater_than_number(column_name="weight", number=0.14))
builder.add_constraint(mean_between_range(column_name="weight", lower=2, upper=3))
builder.add_constraint(smaller_than_number(column_name="weight", number=20.5))
builder.add_constraint(stddev_between_range(column_name="weight", lower=1, upper=3))
builder.add_constraint(quantile_between_range(column_name="weight", quantile=0.5, lower=1.5, upper=2.0))
builder.add_constraint(is_in_range(column_name="weight", lower=1.1, upper=3.2))
builder.add_constraint(is_in_range(column_name="legs", lower=0, upper=6))
builder.add_constraint(is_non_negative(column_name="legs"))

# animal has missing distribution metrics. this will pass if skip_missing = True and fail otherwise.
builder.add_constraint(
    quantile_between_range(
        column_name="animal", 
        quantile=0.5, 
        lower=1.5, 
        upper=2.0, 
        skip_missing=False
    )
)

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)


# ## Frequent Items/Frequent Strings Constraints <a class="anchor" id="frequent"></a>

# In[5]:


from whylogs.core.constraints.factories import n_most_common_items_in_set, frequent_strings_in_reference_set


# ### Examples - Frequent Items/Frequent Strings Constraints

# In[6]:


import whylogs as why
import pandas as pd
data = {
    "animal": ["cat", "snake", "snake", "cat", "mosquito"],
    "legs": [0, 1, 2, 3, 4],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
}

results = why.log(pd.DataFrame(data))
profile_view = results.view()


# In[7]:


builder = ConstraintsBuilder(dataset_profile_view=profile_view)
reference_set = {"cat","snake"}
builder.add_constraint(frequent_strings_in_reference_set(column_name="animal", reference_set=reference_set))
builder.add_constraint(n_most_common_items_in_set(column_name="animal",n=2,reference_set=reference_set))

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)


# ## Counters Constraints <a class="anchor" id="counts"></a>

# In[8]:


from whylogs.core.constraints.factories import no_missing_values, count_below_number, null_percentage_below_number, null_values_below_number


# ### Examples - Counters Constraints

# In[9]:


import whylogs as why
import pandas as pd
data = {
    "animal": ["cat", "snake", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, None, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
}

results = why.log(pd.DataFrame(data))
profile_view = results.view()


# In[10]:


builder = ConstraintsBuilder(dataset_profile_view=profile_view)
builder.add_constraint(count_below_number(column_name="legs", number=10))
builder.add_constraint(null_percentage_below_number(column_name="legs", number=0.05))
builder.add_constraint(null_values_below_number(column_name="legs", number=1))
builder.add_constraint(no_missing_values(column_name="legs"))
builder.add_constraint(no_missing_values(column_name="animal"))

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)


# ## Cardinality Constraints <a class="anchor" id="card"></a>

# In[11]:


from whylogs.core.constraints.factories import distinct_number_in_range


# ### Examples - Cardinality Constraints

# In[12]:


import whylogs as why
import pandas as pd
data = {
    "animal": ["cat", "snake", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, None, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
}

results = why.log(pd.DataFrame(data))
profile_view = results.view()


# In[13]:


builder = ConstraintsBuilder(dataset_profile_view=profile_view)
builder.add_constraint(distinct_number_in_range(column_name = "animal", lower = 3, upper = 6))

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)


# ## Types Metrics <a class="anchor" id="types"></a>

# ### Examples - Types Metrics

# In[14]:


import whylogs as why
import pandas as pd

data = {
    "animal": ["cat", "snake", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, None, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
    "flies": [False, False, "False", False, True],
    "obj": [{"a":1}, None, {"a":1}, {"a":1}, {"a":1}]
}
df = pd.DataFrame(data)
results = why.log(df)
profile_view = results.view()


# #### Check Nullable Types

# In[15]:


from whylogs.core.constraints.factories import ( 
    column_is_nullable_integral,
    column_is_nullable_boolean, 
    column_is_nullable_fractional,
    column_is_nullable_object,
    column_is_nullable_string,
)
from whylogs.core.constraints import ConstraintsBuilder


builder = ConstraintsBuilder(dataset_profile_view=profile_view)
builder.add_constraint(column_is_nullable_string(column_name="animal"))
builder.add_constraint(column_is_nullable_integral(column_name="legs"))
builder.add_constraint(column_is_nullable_fractional(column_name="weight"))
builder.add_constraint(column_is_nullable_boolean(column_name="flies"))
builder.add_constraint(column_is_nullable_object(column_name="obj"))

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)


# The constraints above will pass if all values are of a given type. Null values are accepted.
# 
# Note that for `legs`, the constraints failed. That is because whylogs leverages __pandas' dtypes__ when it is available, and when a `None` is present, the column is considered to be `fractional`, even though the remaining values were originally integers. 

# ## Combined Constraints <a class="anchor" id="comb"></a>

# ### Examples - Combined Metrics

# To create a constraint that checks for a non-nullable type, we combine two separate constraints:
# 
# - `column is nullable datatype`
# - `null values below 1`

# In[16]:


import whylogs as why
import pandas as pd

data = {
    "animal": ["cat", "snake", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, None, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
    "flies": [False, False, "False", False, True],
    "obj": [{"a":1}, None, {"a":1}, {"a":1}, {"a":1}]
}
df = pd.DataFrame(data)
results = why.log(df)
profile_view = results.view()


# #### Check Non-nullable Types

# In[17]:


from whylogs.core.constraints.factories import ( 
    column_is_nullable_integral,
    column_is_nullable_boolean, 
    column_is_nullable_fractional,
    column_is_nullable_object,
    column_is_nullable_string,
    null_values_below_number,
)
from whylogs.core.constraints import ConstraintsBuilder


builder = ConstraintsBuilder(dataset_profile_view=profile_view)
builder.add_constraint(column_is_nullable_string(column_name="animal"))
builder.add_constraint(null_values_below_number(column_name="animal",number=1))

# The combination of these metrics makes a check of non-nullable integral
builder.add_constraint(column_is_nullable_integral(column_name="legs"))
builder.add_constraint(null_values_below_number(column_name="legs",number=1))

# The combination of these metrics makes a check of non-nullable fractional
builder.add_constraint(column_is_nullable_fractional(column_name="weight"))
builder.add_constraint(null_values_below_number(column_name="weight",number=1))

# The combination of these metrics makes a check of non-nullable boolean
builder.add_constraint(column_is_nullable_boolean(column_name="flies"))
builder.add_constraint(null_values_below_number(column_name="flies",number=1))

# The combination of these metrics makes a check of non-nullable object
builder.add_constraint(column_is_nullable_object(column_name="obj"))
builder.add_constraint(null_values_below_number(column_name="obj",number=1))

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)