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date,twitter,linkedin,tiktok,instagram | ||
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# Copyright 2024 The PyMC Labs Developers | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
""" | ||
Tests API stability for specific external calls | ||
""" | ||
|
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import matplotlib.pyplot as plt | ||
import pandas as pd | ||
|
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import causalpy as cp | ||
from causalpy.experiments.prepostfit import SyntheticControl | ||
from causalpy.pymc_models import WeightedSumFitter | ||
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|
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def test_causal_inference_and_discovery_with_python_example(): | ||
"""Test example used in Alexander Molak's book 'Causal Inference and Discovery in Python' | ||
Chapter 11 (pages 304-307) | ||
""" | ||
data = pd.read_csv(r"./causalpy/data/gt_social_media_data.csv") | ||
data.index = pd.to_datetime(data["date"]) | ||
data = data.drop("date", axis=1) | ||
treatment_index = pd.to_datetime("2022-10-28") | ||
|
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# Build the model | ||
model = cp.pymc_models.WeightedSumFitter() | ||
assert isinstance( | ||
model, WeightedSumFitter | ||
), "model is not an instance of WeightedSumFitter" | ||
|
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formula = "twitter ~ 0 + tiktok + linkedin + instagram" | ||
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# Run the experiment and plot results | ||
results = cp.pymc_experiments.SyntheticControl( | ||
data, | ||
treatment_index, | ||
formula=formula, | ||
model=model, | ||
) | ||
assert isinstance( | ||
results, SyntheticControl | ||
), "results is not an instance of SyntheticControl" | ||
|
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fig, ax = results.plot() | ||
assert isinstance(fig, plt.Figure) |
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