Time series data fitting with SciPy regression functions

Simple test program to play with SciPy regression functions for time series data

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Time series data

X = np.array([0.10, 0.15, 0.20, 0.30, 0.40, 0.50, 0.58, 0.61, 0.65, 0.80, 0.85, 0.90]).reshape(-1,1)
Y = np.array([0.78, 0.80, 0.76, 0.69, 0.74, 0.71, 0.78, 0.92, 0.80, 0.85, 0.93, 0.98])
pred_X = np.arange(0, 1.1, 0.01).reshape(-1,1)

# Importing the libraries

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn import linear_model
from sklearn.ensemble import RandomForestRegressor 
from sklearn.svm import SVR
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression

from sklearn import gaussian_process
from sklearn.gaussian_process.kernels import RBF,WhiteKernel, ConstantKernel


models = []

models.append(("Ridge Regress:",linear_model.Ridge(alpha=1)))
models.append(("Decision Tree:",DecisionTreeRegressor(max_depth=4)))
models.append(("SVM-gaussian:",SVR(kernel='rbf', C=10, gamma=5,epsilon=0.01)))
models.append(("Random Forest:",RandomForestRegressor(random_state=0)))
models.append(("Neural Net:",MLPRegressor( activation='relu', solver='lbfgs')))
models.append(("AdaBoost:",AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),n_estimators=300, random_state=0)))
models.append(("GradientBoost:",GradientBoostingRegressor(n_estimators=100, learning_rate=0.1,min_samples_leaf=1, max_depth=1, random_state=0, loss='ls')))
models.append(("GaussianProcess:",gaussian_process.GaussianProcessRegressor(kernel=ConstantKernel() * RBF(), normalize_y=True, alpha=0.2)))
models.append(("K-Nearest Neighbour:",KNeighborsRegressor(n_neighbors=3)))


idx=1
fig = plt.figure(figsize=(12,10))
for name,model in models:
    pred_Y = model.fit(X, Y).predict(pred_X)
    plt.subplot(3, len(models)/3, idx)
    plt.plot(X, Y, 'ko') 
    plt.plot(pred_X, pred_Y, color = 'red')
    plt.xlim(0.0, 1.0); plt.ylim(0.6, 1.05)
    plt.title(name, fontsize=18)
    idx+=1

plt.subplots_adjust(hspace = 0.3)