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355 changes: 355 additions & 0 deletions Test_1.ipynb
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\{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MinMaxScalar Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from learnemall.datasets import diabetes\n",
"from learnemall.preprocessing import MinMaxScalar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x,y = diabetes.load_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ms = MinMaxScalar(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_scaled = ms.scale(x)\n",
"print(x_scaled)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(ms.revert(x_scaled))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### StandardScaler Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from learnemall.datasets import diabetes\n",
"from learnemall.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x,y = diabetes.load_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ss = StandardScaler(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_scaled = ss.scale(x)\n",
"print(x_scaled)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(ss.revert(x_scaled))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linear Regression Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from learnemall.datasets import diabetes\n",
"from learnemall.linear import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xtrain,xtest,ytrain,ytest = diabetes.load_data(split=True,ratio=0.75)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xtrain.shape,ytrain.shape,xtest.shape,ytest.shape,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lr = LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = lr.fit(xtrain,ytrain,learning_rate=0.01,num_iters=10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_squared_error(ytrain,lr.predict(xtrain))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_squared_error(ytest, lr.predict(xtest))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logistic Regression Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from learnemall.datasets import iris\n",
"from learnemall.linear import LogisticRegression\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xtrain,xtest,ytrain,ytest = iris.load_data(split=True,ratio=0.75)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xtrain.shape,ytrain.shape,xtest.shape,ytest.shape,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lr = LogisticRegression()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history = lr.fit(xtrain,ytrain,learning_rate=0.01,num_iters=10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"probs = lr.predict_proba(xtrain)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"accuracy_score(ytrain, lr.predict(xtrain))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"accuracy_score(ytest, lr.predict(xtest))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### KNNClassifier Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from learnemall.neighbors import KNNClassifier\n",
"from learnemall.datasets import iris"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xtrain,xtest,ytrain,ytest = iris.load_data(split=True,ratio=0.75)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xtrain.shape,ytrain.shape,xtest.shape,ytest.shape,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"knn = KNNClassifier()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}