diff --git a/1_logistic_regression/Logistic_regression.ipynb b/1_logistic_regression/Logistic_regression.ipynb index cd04d57..5dbcea2 100644 --- a/1_logistic_regression/Logistic_regression.ipynb +++ b/1_logistic_regression/Logistic_regression.ipynb @@ -404,52 +404,128 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load & show the data" + ] + }, + { "cell_type": "code", - "execution_count": 28, + "execution_count": 15, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1797, 64)\n" - ] - }, - { "data": { + "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "from sklearn.datasets import load_digits\n", + "\n", + "# load data\n", + "digits = load_digits()\n", + "\n", + "# copied from notebook 02_sklearn_data.ipynb\n", + "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", + "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", + "\n", + "# plot the digits: each image is 8x8 pixels\n", + "for i in range(64):\n", + " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", + " ax.imshow(digits.images[i], cmap=plt.cm.binary)\n", + " \n", + " # label the image with the target value\n", + " ax.text(0, 7, str(digits.target[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the Data\n", + "\n", + "A good first-step for many problems is to visualize the data using one of the Dimensionality Reduction techniques we saw earlier. We'll start with the most straightforward one, Principal Component Analysis (PCA).\n", + "\n", + "PCA seeks orthogonal linear combinations of the features which show the greatest variance, and as such, can help give you a good idea of the structure of the data set. Here we'll use RandomizedPCA, because it's faster for large N." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" - }, + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2, svd_solver=\"randomized\")\n", + "proj = pca.fit_transform(digits.data)\n", + "\n", + "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A weakness of PCA is that it produces a linear dimensionality reduction:\n", + "this may miss some interesting relationships in the data. If we want to\n", + "see a nonlinear mapping of the data, we can use one of the several\n", + "methods in the `manifold` module. Here we'll use [Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316) (a concatenation\n", + "of Isometric Mapping) which is a manifold learning method based on\n", + "graph theory:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "accuracy train = 0.998608\n", - "accuracy test = 0.897222\n", - "score_train = 0.998608, score_test = 0.897222\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": { @@ -459,18 +535,47 @@ } ], "source": [ + "from sklearn.manifold import Isomap\n", + "iso = Isomap(n_neighbors=5, n_components=2)\n", + "proj = iso.fit_transform(digits.data)\n", + "\n", + "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Program" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n", + "accuracy train = 0.998608, accuracy_test = 0.897222\n", + "score_train = 0.998608, score_test = 0.897222\n" + ] + } + ], + "source": [ "from sklearn.datasets import load_digits\n", + "from sklearn.linear_model.logistic import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", "import matplotlib.pyplot as plt \n", "\n", "# load digital data\n", "digits, dig_label = load_digits(return_X_y=True)\n", "print(digits.shape)\n", "\n", - "# draw one digital\n", - "plt.gray() \n", - "plt.matshow(digits[0].reshape([8, 8])) \n", - "plt.show() \n", - "\n", "# calculate train/test data number\n", "N = len(digits)\n", "N_train = int(N*0.8)\n", @@ -492,11 +597,34 @@ "# calculate train/test accuracy\n", "acc_train = accuracy_score(y_train, pred_train)\n", "acc_test = accuracy_score(y_test, pred_test)\n", - "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test)\n", + "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n", "\n", "score_train = lr.score(x_train, y_train)\n", "score_test = lr.score(x_test, y_test)\n", "print(\"score_train = %f, score_test = %f\" % (score_train, score_test))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", "\n", "# plot confusion matrix\n", "cm = confusion_matrix(y_test,pred_test)\n", diff --git a/1_logistic_regression/Logistic_regression.py b/1_logistic_regression/Logistic_regression.py index 1482ef3..50ab97f 100644 --- a/1_logistic_regression/Logistic_regression.py +++ b/1_logistic_regression/Logistic_regression.py @@ -254,19 +254,72 @@ plt.show() # ## Multi-class recognition +# ### Load & show the data + +# + +import matplotlib.pyplot as plt +from sklearn.datasets import load_digits + +# load data +digits = load_digits() + +# copied from notebook 02_sklearn_data.ipynb +fig = plt.figure(figsize=(6, 6)) # figure size in inches +fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05) + +# plot the digits: each image is 8x8 pixels +for i in range(64): + ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[]) + ax.imshow(digits.images[i], cmap=plt.cm.binary) + + # label the image with the target value + ax.text(0, 7, str(digits.target[i])) +# - + +# ### Visualizing the Data +# +# A good first-step for many problems is to visualize the data using one of the Dimensionality Reduction techniques we saw earlier. We'll start with the most straightforward one, Principal Component Analysis (PCA). +# +# PCA seeks orthogonal linear combinations of the features which show the greatest variance, and as such, can help give you a good idea of the structure of the data set. Here we'll use RandomizedPCA, because it's faster for large N. + +# + +from sklearn.decomposition import PCA +pca = PCA(n_components=2, svd_solver="randomized") +proj = pca.fit_transform(digits.data) + +plt.scatter(proj[:, 0], proj[:, 1], c=digits.target) +plt.colorbar() +# - + +# A weakness of PCA is that it produces a linear dimensionality reduction: +# this may miss some interesting relationships in the data. If we want to +# see a nonlinear mapping of the data, we can use one of the several +# methods in the `manifold` module. Here we'll use [Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316) (a concatenation +# of Isometric Mapping) which is a manifold learning method based on +# graph theory: + +# + +from sklearn.manifold import Isomap +iso = Isomap(n_neighbors=5, n_components=2) +proj = iso.fit_transform(digits.data) + +plt.scatter(proj[:, 0], proj[:, 1], c=digits.target) +plt.colorbar() +# - + +# ## Program + # + from sklearn.datasets import load_digits +from sklearn.linear_model.logistic import LogisticRegression +from sklearn.metrics import accuracy_score + import matplotlib.pyplot as plt # load digital data digits, dig_label = load_digits(return_X_y=True) print(digits.shape) -# draw one digital -plt.gray() -plt.matshow(digits[0].reshape([8, 8])) -plt.show() - # calculate train/test data number N = len(digits) N_train = int(N*0.8) @@ -288,12 +341,17 @@ pred_test = lr.predict(x_test) # calculate train/test accuracy acc_train = accuracy_score(y_train, pred_train) acc_test = accuracy_score(y_test, pred_test) -print("accuracy train = %f, accuracy_test = %f" % (acc_train, acc_test) +print("accuracy train = %f, accuracy_test = %f" % (acc_train, acc_test)) score_train = lr.score(x_train, y_train) score_test = lr.score(x_test, y_test) print("score_train = %f, score_test = %f" % (score_train, score_test)) + + +# + +from sklearn.metrics import confusion_matrix + # plot confusion matrix cm = confusion_matrix(y_test,pred_test) diff --git a/References_notes.md b/References_notes.md index f2d3e6f..ddf01ad 100644 --- a/References_notes.md +++ b/References_notes.md @@ -11,13 +11,12 @@ MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb http://sofasofa.io/competitions.php?type=practice https://www.kaggle.com/competitions -https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md -evaluation metrics -http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb +Titanic +notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb -model selection and assessment -http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb + +## Method Programming Multiclass Logistic Regression http://localhost:8889/notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb @@ -26,4 +25,25 @@ Equation for MLP http://localhost:8889/notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb Optimization methods -http://localhost:8889/notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb \ No newline at end of file +http://localhost:8889/notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb + + +https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md + +evaluation metrics +http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb + + +model selection and assessment +http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb + + +NN +神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317 +零基础入门深度学习(3) - 神经网络和反向传播算法 https://www.zybuluo.com/hanbingtao/note/476663 +如何直观地解释 backpropagation 算法? https://www.zhihu.com/question/27239198 +一文弄懂神经网络中的反向传播法——BackPropagation https://www.cnblogs.com/charlotte77/p/5629865.html + +https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf +https://enlight.nyc/projects/neural-network/ +https://www.python-course.eu/neural_networks_with_python_numpy.php \ No newline at end of file