import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['figure.figsize'] = (10, 5)
plt.style.use('fivethirtyeight')


## Timeseries kinds and applications

### Plotting a time series (I)

In this exercise, you'll practice plotting the values of two time series without the time component.

data = pd.read_csv('./dataset/data.csv', index_col=0)

fig, axs = plt.subplots(2, 1, figsize=(10, 10));
data.iloc[:1000].plot(y='data_values', ax=axs);
data2.iloc[:1000].plot(y='data_values', ax=axs); ### Plotting a time series (II)

You'll now plot both the datasets again, but with the included time stamps for each (stored in the column called "time"). Let's see if this gives you some more context for understanding each time series data.

data = pd.read_csv('./dataset/data_time.csv', index_col=0)

fig, axs = plt.subplots(2, 1, figsize=(10, 10));
data.iloc[:1000].plot(x='time', y='data_values', ax=axs);
data2.iloc[:1000].plot(x='time', y='data_values', ax=axs); ## Machine Learning Basics

• Preparing data for scikit-learn
• scikit-learn expects a particular structure of data: (samples, features)
• Make sure that your data is at least two-dimensional
• Make sure the first dimension is samples
data = pd.read_csv('./dataset/iris_tsa.csv', index_col=0)

sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
50 7.0 3.2 4.7 1.4 1
51 6.4 3.2 4.5 1.5 1
52 6.9 3.1 4.9 1.5 1
53 5.5 2.3 4.0 1.3 1
54 6.5 2.8 4.6 1.5 1
sns.scatterplot(x='petal length (cm)', y='petal width (cm)', hue='target', data=data); from sklearn.svm import LinearSVC

# Construct data for the model
X = data[['petal length (cm)', 'petal width (cm)']]
y = np.ravel(data[['target']])

# Fit the model
model = LinearSVC()
model.fit(X, y)

LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
verbose=0)

### Predicting using a classification model

Now that you have fit your classifier, let's use it to predict the type of flower (or class) for some newly-collected flowers.

Using the classifier you fit, you'll predict the type of each flower.

targets = pd.read_csv('./dataset/iris_target.csv', index_col=0)

X_predict = targets[['petal length (cm)', 'petal width (cm)']]

# Predict with the model
predictions = model.predict(X_predict)
print(predictions)

# Visualize predictions and actual values
plt.scatter(X_predict['petal length (cm)'], X_predict['petal width (cm)'],
c=predictions, cmap=plt.cm.coolwarm);
plt.xlabel('petal length (cm)');
plt.ylabel('petal width (cm)');
plt.title("Predicted class values");

[2 2 2 1 1 2 2 2 2 1 2 1 1 2 1 1 2 1 2 2] ### Fitting a simple model: regression

In this exercise, you'll practice fitting a regression model using data from the Boston housing market.

boston = pd.read_csv('./dataset/boston.csv', index_col=0)

plt.scatter(boston['AGE'], boston['RM']); from sklearn import linear_model

# Prepare input and output DataFrame
X = boston[['AGE']]
y = boston[['RM']]

# Fit the model
model = linear_model.LinearRegression()
model.fit(X, y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

### Predicting using a regression model

Now that you've fit a model with the Boston housing data, lets see what predictions it generates on some new data. You can investigate the underlying relationship that the model has found between inputs and outputs by feeding in a range of numbers as inputs and seeing what the model predicts for each input.

new_inputs = np.array(pd.read_csv('./dataset/boston_newinputs.csv', index_col=0, header=None).values)

predictions = model.predict(new_inputs)

# Visualizae the inputs and predicted values
plt.scatter(new_inputs, predictions, color='r', s=3);
plt.xlabel('inputs');
plt.ylabel('predictions'); ## Machine learning and time series data

### Inspecting the classification data

In these final exercises of this chapter, you'll explore the two datasets you'll use in this course.

The first is a collection of heartbeat sounds. Hearts normally have a predictable sound pattern as they beat, but some disorders can cause the heart to beat abnormally. This dataset contains a training set with labels for each type of heartbeat, and a testing set with no labels. You'll use the testing set to validate your models.

As you have labeled data, this dataset is ideal for classification. In fact, it was originally offered as a part of a public Kaggle competition.

import librosa as lr
from glob import glob

# List all the wav files in the folder
audio_files = glob('./dataset/files/*.wav')

# Read in the first audio file, create the time array
time = np.arange(0, len(audio)) / sfreq

# Plot audio over time
fig, ax = plt.subplots();
ax.plot(time, audio)
ax.set(xlabel='Time (s)', ylabel='Sound Amplitude');
plt.savefig('../images/murmur_sound.png') ### Inspecting the regression data

The next dataset contains information about company market value over several years of time. This is one of the most popular kind of time series data used for regression. If you can model the value of a company as it changes over time, you can make predictions about where that company will be in the future. This dataset was also originally provided as part of a public Kaggle competition.

In this exercise, you'll plot the time series for a number of companies to get an understanding of how they are (or aren't) related to one another.

data = pd.read_csv('./dataset/prices_nyse.csv', index_col=0)

# Convert the index of the DataFrame to datetime
data.index = pd.to_datetime(data.index)

# Loop through each column, plot its values over time
fig, ax = plt.subplots();
for column in data.columns:
data[column].plot(ax=ax, label=column);
ax.legend();

                  AAPL  FB       NFLX          V        XOM
time
2010-01-04  214.009998 NaN  53.479999  88.139999  69.150002
2010-01-05  214.379993 NaN  51.510001  87.129997  69.419998
2010-01-06  210.969995 NaN  53.319999  85.959999  70.019997
2010-01-07  210.580000 NaN  52.400001  86.760002  69.800003
2010-01-08  211.980005 NaN  53.300002  87.000000  69.519997 