added tutorial examples and my data workthrough examplesin unsup_v1.py

umap_fs.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 23 13:36:46 2022
+
+@author: tanu
+"""
+#https://umap-learn.readthedocs.io/en/latest/auto_examples/plot_feature_extraction_classification.html
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.pipeline import Pipeline
+from sklearn.svm import LinearSVC
+
+from umap import UMAP
+
+# Make a toy dataset
+X, y = make_classification(
+    n_samples=1000,
+    n_features=300,
+    n_informative=250,
+    n_redundant=0,
+    n_repeated=0,
+    n_classes=2,
+    random_state=1212,
+)
+
+# Split the dataset into a training set and a test set
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# Classification with a linear SVM
+svc = LinearSVC(dual=False, random_state=123)
+params_grid = {"C": [10 ** k for k in range(-3, 4)]}
+clf = GridSearchCV(svc, params_grid)
+clf.fit(X_train, y_train)
+print(
+    "Accuracy on the test set with raw data: {:.3f}".format(clf.score(X_test, y_test))
+)
+
+# Transformation with UMAP followed by classification with a linear SVM
+umap = UMAP(random_state=456)
+pipeline = Pipeline([("umap", umap), ("svc", svc)])
+params_grid_pipeline = {
+    "umap__n_neighbors": [5, 20],
+    "umap__n_components": [15, 25, 50],
+    "svc__C": [10 ** k for k in range(-3, 4)],
+}
+
+
+clf_pipeline = GridSearchCV(pipeline, params_grid_pipeline)
+clf_pipeline.fit(X_train, y_train)
+print(
+    "Accuracy on the test set with UMAP transformation: {:.3f}".format(
+        clf_pipeline.score(X_test, y_test)
+    )
+)