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ChE765Project.py

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+import numpy as np
+import scipy as sp
+import pandas as pd
+import seaborn as sns
+import time
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+import umap
+
+import sklearn.discriminant_analysis
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import r2_score
+from sklearn.cross_decomposition import PLSRegression as pls
+
+training_data = pd.read_excel('PV_train_nonZero.xls')
+
+tc = training_data['Label']
+tv = training_data.drop('Label', axis=1)
+
+# separation of class labels and (non-zero)
+
+training_class = tc.to_numpy()
+training_vals = tv.to_numpy()
+
+# standardize/scale dataset
+
+X = StandardScaler().fit_transform(training_vals)
+
+# PCA dimensionality red/vis; n_components 3
+
+principal1 = PCA(n_components=3)
+pca_3comp = principal1.fit_transform(X)
+
+pca_3comp = np.vstack((pca_3comp.T, training_class)).T
+principal1_DF = pd.DataFrame({
+    'PCA Component 1':pca_3comp[:,0],
+    'PCA Component 2':pca_3comp[:,1],
+    'PCA Component 3':pca_3comp[:,2],
+    'Label':pca_3comp[:,3]})
+
+fig_principal1 = plt.figure(figsize=(10,10))
+
+sns.scatterplot(
+    x='PCA Component 1', y='PCA Component 2',
+    hue='Label',
+    palette=sns.color_palette("hls",3),
+    data=principal1_DF,
+    alpha=0.5,
+    legend=False
+)
+
+# PCA dimensionality red/vis; n_components 4
+
+time_start = time.time()
+
+principal2 = PCA(n_components=4)
+pca_4comp = principal2.fit_transform(X)
+
+print('\nPCA compute time: {} seconds'.format(time.time()-time_start))
+
+pca_4comp = np.vstack((pca_4comp.T, training_class)).T
+principal2_DF = pd.DataFrame({
+    'PCA Component 1 (n=4)':pca_4comp[:,0],
+    'PCA Component 2 (n=4)':pca_4comp[:,1],
+    'PCA Component 3 (n=4)':pca_4comp[:,2], 
+    'PCA Component 4 (n=4)':pca_4comp[:,3],
+    'Label':pca_4comp[:,4]}
+)
+
+print('\nExplained variation per principal component: {}\n'.format(principal2.explained_variance_ratio_))
+
+# tSNE
+# PCA init, early_exaggeration yields no discernible benefit 
+
+time_start = time.time()
+
+tsne = TSNE(
+    n_components=3,
+    verbose=1,
+    method='barnes_hut',
+    metric='euclidean',
+    learning_rate=10,
+    angle=0.2,
+
+    # variables
+    perplexity=110,
+    early_exaggeration=60,
+    n_iter=1000
+)
+
+tsne_output=tsne.fit_transform(X)
+
+print('\ntSNE compute time: {} seconds'.format(time.time()-time_start))
+
+tsne_df=pd.DataFrame({'t-SNE Component 1':tsne_output[:,0],'t-SNE Component 2':tsne_output[:,1], 'Label':pca_3comp[:,3]})
+
+fig_tsne=plt.figure(figsize=(10,10))
+sns.scatterplot(
+    x="t-SNE Component 1", y ="t-SNE Component 2",
+    hue="Label",
+    palette=sns.color_palette("hls",3),
+    data=tsne_df,
+    alpha=0.3,
+    legend=False
+)
+
+# # UMAP in 2D space
+
+# time_start=time.time()
+
+# umap=umap.UMAP(
+#     n_components=3,
+#     metric='euclidean',
+#     spread=1,
+#     learning_rate=10,
+#     verbose=1,
+
+#     # variables
+#     n_neighbors=50,
+#     min_dist=0.50,
+#     transform_queue_size=200
+# )
+
+# umap_output=umap.fit_transform(X)
+
+# print('\nUMAP compute time: {} seconds\n'.format(time.time()-time_start))
+
+# umap_df=pd.DataFrame({'UMAP Component 1':umap_output[:,0],'UMAP Component 2':umap_output[:,1], 'Label':pca_3comp[:,3]})
+
+# fig_umap=plt.figure(figsize=(10,10))
+# sns.scatterplot(
+#     x="UMAP Component 1", y="UMAP Component 2",
+#     hue="Label",
+#     palette=sns.color_palette("hls",3),
+#     data=umap_df,
+#     alpha=0.3,
+#     legend=False
+# )
+
+plt.show()

ChE765Project_alt.py

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+import numpy as np
+import scipy as sp
+import pandas as pd
+import seaborn as sns
+import time
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+import umap
+
+import sklearn.discriminant_analysis
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import r2_score
+from sklearn.cross_decomposition import PLSRegression as pls
+
+training_data = pd.read_excel('PV_train_nonZero_alt.xls')
+
+tc = training_data['Label']
+tv = training_data.drop('Label', axis=1)
+
+print (tc)
+# separation of class labels and (non-zero)
+
+training_class = tc.to_numpy()
+training_vals = tv.to_numpy()
+
+# standardize/scale dataset
+
+X = StandardScaler().fit_transform(training_vals)
+
+# PCA dimensionality red/vis; n_components 3
+
+principal1 = PCA(n_components=3)
+pca_3comp = principal1.fit_transform(X)
+
+pca_3comp = np.vstack((pca_3comp.T, training_class)).T
+principal1_DF = pd.DataFrame({
+    'PCA Component 1':pca_3comp[:,0],
+    'PCA Component 2':pca_3comp[:,1],
+    'PCA Component 3':pca_3comp[:,2],
+    'Label':pca_3comp[:,3]})
+
+fig_principal1 = plt.figure(figsize=(10,10))
+
+sns.scatterplot(
+    x='PCA Component 1', y='PCA Component 2',
+    hue='Label',
+    palette=sns.color_palette("dark",2),
+    data=principal1_DF,
+    alpha=0.5,
+    legend=False
+)
+
+# PCA dimensionality red/vis; n_components 4
+
+time_start = time.time()
+
+principal2 = PCA(n_components=4)
+pca_4comp = principal2.fit_transform(X)
+
+print('\nPCA compute time: {} seconds'.format(time.time()-time_start))
+
+pca_4comp = np.vstack((pca_4comp.T, training_class)).T
+principal2_DF = pd.DataFrame({
+    'PCA Component 1 (n=4)':pca_4comp[:,0],
+    'PCA Component 2 (n=4)':pca_4comp[:,1],
+    'PCA Component 3 (n=4)':pca_4comp[:,2], 
+    'PCA Component 4 (n=4)':pca_4comp[:,3],
+    'Label':pca_4comp[:,4]}
+)
+
+print('\nExplained variation per principal component: {}\n'.format(principal2.explained_variance_ratio_))
+
+# tSNE
+# PCA init, early_exaggeration yields no discernible benefit 
+
+time_start = time.time()
+
+tsne = TSNE(
+    n_components=2,
+    verbose=1,
+    method='barnes_hut',
+    metric='euclidean',
+    learning_rate=50,
+    angle=0.2,
+
+    # variables
+    perplexity=400,
+    early_exaggeration=60,
+    n_iter=5000
+)
+
+tsne_output=tsne.fit_transform(X)
+
+print('\ntSNE compute time: {} seconds'.format(time.time()-time_start))
+
+tsne_df=pd.DataFrame({'t-SNE Component 1':tsne_output[:,0],'t-SNE Component 2':tsne_output[:,1], 'Label':pca_3comp[:,3]})
+tsne_df.to_csv("./tsne_output.csv", sep = '\t', index = True, header = True)
+
+fig_tsne=plt.figure(figsize=(10,10))
+sns.scatterplot(
+    x="t-SNE Component 1", y ="t-SNE Component 2",
+    hue="Label",
+    palette=sns.color_palette("dark",2),
+    data=tsne_df,
+    alpha=0.3,
+    legend=False
+)
+
+# UMAP in 2D space
+
+time_start=time.time()
+
+umap=umap.UMAP(
+    n_components=2,
+    metric='euclidean',
+    spread=1,
+    learning_rate=10,
+    verbose=1,
+
+    # variables
+    n_neighbors=50,
+    min_dist=0.50,
+    transform_queue_size=800
+)
+
+umap_output=umap.fit_transform(X)
+
+print('\nUMAP compute time: {} seconds\n'.format(time.time()-time_start))
+
+umap_df=pd.DataFrame({'UMAP Component 1':umap_output[:,0],'UMAP Component 2':umap_output[:,1], 'Label':pca_3comp[:,3]})
+umap_df.to_csv("./umap_output.csv", sep = '\t', index = True, header = True)
+
+fig_umap=plt.figure(figsize=(10,10))
+sns.scatterplot(
+    x="UMAP Component 1", y="UMAP Component 2",
+    hue="Label",
+    palette=sns.color_palette("dark",2),
+    data=umap_df,
+    alpha=0.3,
+    legend=False
+)
+
+plt.show()

ChE765Project_alt_classifier.py

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+import numpy as np
+import scipy as sp
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import classification_report, confusion_matrix
+
+data = pd.read_excel('tsne_output.xls')
+
+X = data.drop('Label', axis=1)
+y = data['Label']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
+
+# SVM polynomial kernel
+
+svc = SVC(kernel='poly', degree=10)
+svc.fit(X_train, y_train)
+
+y_pred = svc.predict(X_test)
+
+print('\nPolynomial Kernel')
+print(confusion_matrix(y_test, y_pred))
+print(classification_report(y_test, y_pred))
+
+# SVM sigmoid kernel
+
+svc = SVC(kernel='sigmoid')
+svc.fit(X_train, y_train)
+
+y_pred = svc.predict(X_test)
+
+print('\nSigmoid Kernel')
+print(confusion_matrix(y_test, y_pred))
+print(classification_report(y_test, y_pred))
+
+# SVM Guassian kernel
+
+svc = SVC(kernel='rbf', gamma=10)
+svc.fit(X_train, y_train)
+
+y_pred = svc.predict(X_test)
+
+print('\nRBF Kernel')
+print(confusion_matrix(y_test, y_pred))
+print(classification_report(y_test, y_pred))
+
+plt.show()