linted code

Topological_ML/tda_function.py

+import sklearn
+from sklearn.linear_model import LinearRegression
+import kmapper as km
+import pandas as pd
+import numpy as np
+
+def numpy_to_pandas(sklearn_data):
+    """
+    Converts scikit-learn numpy data into pandas dataframe.
+    Input: name of dataframe
+    Output: pandas dataframe
+    """
+    data = pd.DataFrame(data=sklearn_data.data, columns=sklearn_data.feature_names)
+    data['target'] = pd.Series(sklearn_data.target)
+    return data
+
+def linear_regression(feature, predictor):
+    """
+    Ordinary least squares Linear Regression.
+    input: x = independent variables
+           y = dependent variable
+    output: R^2
+    """
+    model = LinearRegression()
+    model.fit(feature, predictor)
+    return model.score(feature, predictor)
+
+def lens_1d(features, random_num, verbosity):
+    """
+    input: 
+    output:
+    """
+    model = sklearn.ensemble.IsolationForest(random_state=random_num)
+    model.fit(features)
+    lens1 = model.decision_function(features).reshape((features.shape[0], 1))
+    mapper = km.KeplerMapper(verbose=verbosity)
+    lens2 = mapper.fit_transform(features, projection="l2norm")
+    lens = np.c_[lens1, lens2]
+    return lens
+
+def county_crosstab(data, county, year, index, columns):
+    """
+    input:
+    output:
+    """
+    subset_df = data[data.year == year]
+    sub_df = subset_df[subset_df.county == county]
+    crosstab = pd.crosstab(index=sub_df[index], columns=sub_df[columns])
+    return crosstab