import numpy as np import matplotlib.pyplot as plt from sklearn.neural_network import MLPClassifier from sklearn.datasets import load_iris from pandas import DataFrame import pandas as pd import seaborn as sns

# Input data into dataframe iris=load_iris() X=iris.data # make data frame df=DataFrame(X) # add column with targets df.columns=iris['feature_names'] df['target']=iris['target'] #add species name for plotting df['species']=df['target'].map({0:iris.target_names[0],1:iris.target_names[1],2:iris.target_names[2]}) df.head()

# Create NumPy target array y=df.target.values

# Split data in test and train sets using 70-30 split # from sklearn.model_selection import train_test_split y=df.target.values (X_train,X_test,y_train,y_test)=train_test_split(X,y,test_size=.3) n_train=len(X_train) n_test=len(X_test) print(n_train,n_test)

# ANN sensitive to scaling so scale # # Import StandardScaler and scale from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test)

# Create classifier and train mlp=MLPClassifier(max_iter=1000,solver='adam',random_state=1) mlp.fit(X_train,y_train) # Print out precent accuracy of training set print("Percent accuracy for training data is",100*mlp.score(X_train,y_train))

# Print out percent accuracy of test set predictions=mlp.predict(X_test) print("Percent accuracy for test data is",100*mlp.score(X_test,y_test))

# Scale the entire data X scaler.fit(X) X = scaler.transform(X)

# Predict the entire dataset and add column to dataframe for Prediction and predicted species pred=mlp.predict(X) df['Prediction']=pred

# Plot petal length vs sepal length for actual data with hue = species and Prediction; compare sns.relplot(x='petal length (cm)',y='sepal length (cm)',data=df,hue='species')

# Plot petal width vs sepal width for actual data with hue = species and Prediction; compare sns.relplot(x='petal width (cm)',y='sepal width (cm)',data=df,hue='species')