Move matplotlib to an optional dependency (#243)
* removed matplotlib Signed-off-by: Amit Sharma <amit_sharma@live.com> * added new req file Signed-off-by: Amit Sharma <amit_sharma@live.com>
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d3245908b1
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@ -28,6 +28,7 @@ jobs:
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python -m pip install --upgrade pip
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pip install flake8 pytest twine
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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if [ -f requirements-plotting.txt ]; then pip install -r requirements-plotting.txt; fi
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pip install causalml nbformat jupyter
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pip install econml
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- name: Lint with flake8
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@ -3,8 +3,6 @@ import logging
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import numpy as np
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import pandas as pd
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import matplotlib
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import matplotlib.pyplot as plt
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import math
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import statsmodels.api as sm
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@ -86,6 +84,8 @@ class AddUnobservedCommonCause(CausalRefuter):
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self.logger.debug(refute)
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results_matrix[i][j] = refute.estimated_effect # Populate the results
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import matplotlib
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=(6,5))
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left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
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ax = fig.add_axes([left, bottom, width, height])
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@ -115,6 +115,8 @@ class AddUnobservedCommonCause(CausalRefuter):
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self.logger.debug(refute)
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outcomes[i] = refute.estimated_effect # Populate the results
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import matplotlib
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=(6,5))
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left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
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ax = fig.add_axes([left, bottom, width, height])
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@ -142,6 +144,8 @@ class AddUnobservedCommonCause(CausalRefuter):
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self.logger.debug(refute)
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outcomes[i] = refute.estimated_effect # Populate the results
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import matplotlib
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import matplotlib.pyplot as plt
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fig = plt.figure(figsize=(6,5))
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left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
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ax = fig.add_axes([left, bottom, width, height])
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@ -1,5 +1,4 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from dowhy.interpreters.visual_interpreter import VisualInterpreter
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from dowhy.causal_estimators.propensity_score_weighting_estimator import PropensityScoreWeightingEstimator
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@ -83,6 +82,7 @@ class ConfounderDistributionInterpreter(VisualInterpreter):
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title1 = "Distribution of " + self.var_name + " before applying the weights"
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title2 = "Distribution of " + self.var_name + " after applying the weights"
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import matplotlib.pyplot as plt
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=self.fig_size)
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iterable = zip([barplot_df_before, barplot_df_after], [ax1, ax2], [title1, title2])
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for plot_df, ax, title in iterable:
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@ -1,6 +1,5 @@
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from dowhy.interpreters.visual_interpreter import VisualInterpreter
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from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
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@ -61,6 +60,8 @@ class PropensityBalanceInterpreter(VisualInterpreter):
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mean_diff_strata["sample"]="PropensityAdjusted"
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mean_diff_overall["sample"] = "Unadjusted"
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plot_df = pd.concat([mean_diff_overall, mean_diff_strata])
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import matplotlib.pyplot as plt
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plt.style.use("seaborn-white")
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fig, ax = plt.subplots(1,1)
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for label, subdf in plot_df.groupby('common_cause_id'):
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@ -0,0 +1 @@
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matplotlib
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@ -3,7 +3,6 @@ scipy
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statsmodels
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pandas>=0.24
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networkx>=2.0
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matplotlib
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sympy>=1.4
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scikit-learn
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pydot>=1.4
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9
setup.py
9
setup.py
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@ -19,6 +19,14 @@ with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
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with open(path.join(here, 'requirements.txt'), encoding='utf-8') as f:
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install_requires = f.read().splitlines()
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# Plotting packages are optional to install
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extras = ["plotting"]
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extras_require = dict()
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for e in extras:
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req_file = "requirements-{0}.txt".format(e)
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with open(req_file) as f:
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extras_require[e] = [line.strip() for line in f]
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# Loading version number
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with open(path.join(here, 'dowhy', 'VERSION')) as version_file:
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version = version_file.read().strip()
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@ -45,6 +53,7 @@ setup(
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packages=find_packages(exclude=['docs', 'tests']),
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python_requires='>=3.5',
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install_requires=install_requires,
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extras_require=extras_require,
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include_package_data=True,
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package_data={'dowhy':['VERSION']}
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)
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