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Copy pathore_python_integration.py
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141 lines (102 loc) · 4.26 KB
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import numpy as np
from sklearn.linear_model import Ridge
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neighbors import NearestNeighbors
from sklearn.tree import plot_tree
from pyearth import Earth
import hnswlib as hnswlib
import matplotlib.pyplot as plt
def flatten_list(l):
return [x for u in l for x in u]
def conditional_expectation(x,y):
X = np.array(x)
Y = np.array(y)
# 1a linear regression
# poly = PolynomialFeatures(degree=2)
# X_pre = poly.fit_transform(X)
# model = LinearRegression()
# model.fit(X_pre, Y)
# return flatten_list(model.predict(X_pre).tolist())
# 1b ridge regression
#poly = PolynomialFeatures(degree=4)
#X_pre = poly.fit_transform(X)
#model = Ridge()
#model.fit(X_pre, Y)
#return model.predict(X_pre).tolist()
# 2 regression tree
# model = DecisionTreeRegressor(max_depth = 5)
# model.fit(X, Y)
# return model.predict(X).tolist()
# 3 k-nearest neighbours
# model = KNeighborsRegressor(n_neighbors=100)
# model.fit(X, Y)
# return flatten_list(model.predict(X).tolist())
# 4 Fourier
# X = MinMaxScaler().fit_transform(np.array(x))
# def fourier_features(X, order=40):
# return np.column_stack([np.sin(k * np.pi * X[:, i]) for i in range(X.shape[1]) for k in range(1, order + 1)] +
# [np.cos(k * np.pi * X[:, i]) for i in range(X.shape[1]) for k in range(1, order + 1)])
# X_fourier = fourier_features(X)
# ridge = Ridge(alpha=0.00001).fit(X_fourier, np.array(y))
# pred = ridge.predict(X_fourier)
# #rmse = np.sqrt(mean_squared_error(y, pred))
# #print(f'RMSE: {rmse}')
# return pred.tolist()
# 5 Fourier + Glättung
# X = MinMaxScaler().fit_transform(np.array(x))
# def fourier_features(X, order=20):
# return np.column_stack(
# [np.sin(k * np.pi * X[:, i]) for i in range(X.shape[1]) for k in range(1, order + 1)] +
# [np.cos(k * np.pi * X[:, i]) for i in range(X.shape[1]) for k in range(1, order + 1)]
# )
# X_fourier = fourier_features(X)
# ridge = Ridge(alpha=0.00001).fit(X_fourier, np.array(y))
# pred = ridge.predict(X_fourier)
# # Glättung im R^3 Raum mit gewichteter 5-NN (vektorisiert)
# nbrs = NearestNeighbors(n_neighbors=6).fit(X) # Punkt selbst + 5 Nachbarn
# _, indices = nbrs.kneighbors(X)
# # Gewichtsmatrix: 0.5 für den Punkt selbst, 0.1 für die 5 Nachbarn
# weights = np.full(indices.shape, 0.1)
# weights[:, 0] = 0.5 # erster Nachbar ist immer der Punkt selbst
# # Wende die Gewichtung vektorisiert an
# pred_array = np.array(pred)
# smoothed_pred = np.sum(weights * pred_array[indices], axis=1)
# return smoothed_pred.tolist()
# 6 hnswlib
# k = 5
# ef = 100
# """
# Berechnung der bedingten Erwartung ohne Gewichtung (kein smoothing),
# einfaches arithmetisches Mittel der k nächsten Nachbarn.
# Parameters:
# - x: 2D-Array der Eingabedaten
# - y: 1D-Array der Zielwerte
# - k: Anzahl der Nachbarn
# - ef: HNSW-Suchparameter
# Returns:
# - avg_preds: Array der Durchschnittswerte der k-Nachbarn
# """
# X = MinMaxScaler().fit_transform(np.array(x))
# pred = np.array(y)
# dim = X.shape[1]
# # Initialisiere HNSW-Index
# index = hnswlib.Index(space='l2', dim=dim)
# index.init_index(max_elements=len(X), ef_construction=ef, M=16)
# index.add_items(X)
# index.set_ef(ef)
# # KNN-Abfrage
# labels, _ = index.knn_query(X, k=min(k, len(X)))
# # Durchschnitt der Zielwerte der k Nachbarn (ungewichtet)
# neighbor_targets = pred[labels]
# avg_preds = np.mean(neighbor_targets, axis=1)
# return flatten_list(avg_preds)
# MARS (py-earth2)
#model = Earth(use_fast=True, fast_K=20, fast_h=1, max_degree=1, max_terms=20, minspan=0, penalty=0)#, endspan=0, tresh=0.00001)
model = Earth(use_fast=True, fast_K=20, fast_h=1, max_degree=1, max_terms=20)
model.fit(X,Y)
return model.predict(X).tolist()