80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
# Regression/Prediction (Totally gonna do later trust bro)
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from sklearn.linear_model import LinearRegression
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from sklearn.impute import SimpleImputer
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from sklearn import preprocessing
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from scipy.spatial import distance
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import scipy.stats as stats
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import numpy as np
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import pandas as pd
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def cure_depression(dataset):
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# this is pog
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numeric = dataset.select_dtypes(include=np.number)
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numeric_columns = numeric.columns
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dataset[numeric_columns] = dataset[numeric_columns].interpolate(
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method="linear", limit_direct="forward"
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)
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# fuck around and find out with other methods maybe idk
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return dataset
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# Fuck you and whateevr you fucking stand for you dumb whore faggot
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def regression_expression(dataset, column, missing_value):
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lr = LinearRegression()
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numeric = dataset.select_dtypes(include=np.number)
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# Migrate this to digger
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# the fookin nulls
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testdf = numeric[numeric[column].isnull() == False]
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testdf = testdf[testdf[column] != 0]
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# the non nulls and non 0s
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traindf = numeric[numeric[column].isnull() == False]
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traindf = traindf[traindf[column] != 0]
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# print(traindf.head(20))
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# end of migration
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y = traindf[column]
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traindf.drop(column, axis=1, inplace=True)
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lr.fit(traindf, y)
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pred = lr.predict(testdf)
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# can't put this in data set directly because length no match
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# join testdf and traindf to form dataset perhaps??
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testdf[column] = pred
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print(testdf.head(30))
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# https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
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# That helps ^
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# This boi should work, idk i'm implementing blindly
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def scaling_zscore(dataframe, col):
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return stats.zscore(dataframe[col],axis = 0, nan_policy= "omit")
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def dissimilarity(row_arr):
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row_arr = row_arr.select_dtypes(include = np.number)
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row_arr = row_arr.drop('Rank', axis = 1)
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print(" Dissim | Entry 1 | Entry 2 | Entry 3 | Entry 4 | Entry 5 |")
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for i in range(len(row_arr)):
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print("Entry " , i + 1, " | ", end = "")
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for j in range(len(row_arr)):
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eucDist = distance.euclidean(row_arr.iloc[i], row_arr.iloc[j])
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print(" {:#.6g} |".format(eucDist), end = "")
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print("\n")
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def similarity(row_arr):
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row_arr = row_arr.select_dtypes(include = np.number)
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row_arr = row_arr.drop('Rank', axis = 1)
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print("Similarity| Entry 1 | Entry 2 | Entry 3 | Entry 4 | Entry 5 |")
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for i in range(len(row_arr)):
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print("Entry ", i + 1, " | ", end = "")
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for j in range(len(row_arr)):
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sim = 1 - distance.cosine(row_arr.iloc[i], row_arr.iloc[j])
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print(" {:#.6g} |".format(sim), end = "")
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print("\n")
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def scaling_range(datashitter, col):
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nonnull = datashitter[col].isna()
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minmax_scaler = preprocessing.MinMaxScaler()
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trainer = minmax_scaler.fit_transform(datashitter[nonnull])
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return minmax_scaler.transform(datashitter[col])
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