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Commit 7da9b33a authored by Zeynep Hakguder's avatar Zeynep Hakguder
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%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# JUPYTER NOTEBOOK TIPS # JUPYTER NOTEBOOK TIPS
Each rectangular box is called a cell. Each rectangular box is called a cell.
* Ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text. * Ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
* Alt+ENTER evaluates the current cell and adds a new cell below it. * Alt+ENTER evaluates the current cell and adds a new cell below it.
* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue. * If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
# Supervised Learning Model Skeleton # Supervised Learning Model Skeleton
We'll use this skeleton for implementing different supervised learning algorithms. We'll use this skeleton for implementing different supervised learning algorithms.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
class Model: class Model:
def fit(self): def fit(self):
raise NotImplementedError raise NotImplementedError
def predict(self, test_points): def predict(self, test_points):
raise NotImplementedError raise NotImplementedError
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def preprocess(feature_file, label_file): def preprocess(feature_file, label_file):
''' '''
Args: Args:
feature_file: str feature_file: str
file containing features file containing features
label_file: str label_file: str
file containing labels file containing labels
Returns: Returns:
features: ndarray features: ndarray
nxd features nxd features
labels: ndarray labels: ndarray
nx1 labels nx1 labels
''' '''
# read in features and labels # read in features and labels
features = np.genfromtxt(feature_file)
labels = np.genfromtxt(label_file)
return features, labels return features, labels
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
def partition(size, t, v = 0): def partition(size, t, v = 0):
''' '''
Args: Args:
size: int size: int
number of examples in the whole dataset number of examples in the whole dataset
t: float t: float
proportion kept for test proportion kept for test
v: float v: float
proportion kept for validation proportion kept for validation
Returns: Returns:
test_indices: ndarray test_indices: ndarray
1D array containing test set indices 1D array containing test set indices
val_indices: ndarray val_indices: ndarray
1D array containing validation set indices 1D array containing validation set indices
''' '''
# number of test and validation examples # number of test and validation examples
t_size = np.int(np.ceil(size*t))
v_size = np.int(np.ceil(size*v))
# shuffle the indices
permuted = np.random.permutation(size)
# spare the first t_size for test
test_indices = permuted[:t_size]
# and the next v_size for validation
val_indices = permuted[t_size+1:t_size+v_size+1]
train_indices = np.delete(np.arange(size), np.append(test_indices, val_indices), 0)
return test_indices, val_indices, train_indices return test_indices, val_indices, train_indices
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
## TASK 1: Implement `distance` function ## TASK 1: Implement `distance` function
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
"distance" function will be used in calculating cost of *k*-NN. It should take two data points and the name of the metric and return a scalar value. "distance" function will be used in calculating cost of *k*-NN. It should take two data points and the name of the metric and return a scalar value.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#TODO: Programming Assignment 1 #TODO: Programming Assignment 1
def distance(x, y, metric): def distance(x, y, metric):
''' '''
Args: Args:
x: ndarray x: ndarray
1D array containing coordinates for a point 1D array containing coordinates for a point
y: ndarray y: ndarray
1D array containing coordinates for a point 1D array containing coordinates for a point
metric: str metric: str
Euclidean, Manhattan Euclidean, Manhattan
Returns: Returns:
dist: float dist: float
''' '''
if metric == 'Euclidean': if metric == 'Euclidean':
raise NotImplementedError raise NotImplementedError
elif metric == 'Manhattan': elif metric == 'Manhattan':
raise NotImplementedError raise NotImplementedError
else: else:
raise ValueError('{} is not a valid metric.'.format(metric)) raise ValueError('{} is not a valid metric.'.format(metric))
return dist # scalar distance btw x and y return dist # scalar distance btw x and y
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
## General supervised learning performance related functions ## General supervised learning performance related functions
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Implement the "conf_matrix" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*). It should output a numpy.ndarray. Implement the "conf_matrix" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*). It should output a numpy.ndarray.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# TODO: Programming Assignment 1 # TODO: Programming Assignment 1
def conf_matrix(true, pred, n_classes): def conf_matrix(true, pred, n_classes):
''' '''
Args: Args:
true: ndarray true: ndarray
nx1 array of true labels for test set nx1 array of true labels for test set
pred: ndarray pred: ndarray
nx1 array of predicted labels for test set nx1 array of predicted labels for test set
n_classes: int n_classes: int
Returns: Returns:
result: ndarray result: ndarray
n_classes x n_classes array confusion matrix n_classes x n_classes array confusion matrix
''' '''
raise NotImplementedError raise NotImplementedError
result = np.ndarray([n_classes, n_classes]) result = np.ndarray([n_classes, n_classes])
# returns the confusion matrix as numpy.ndarray # returns the confusion matrix as numpy.ndarray
return result return result
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
ROC curves are a good way to visualize sensitivity vs. 1-specificity for varying cut off points. "ROC" takes a list containing different *threshold* parameter values to try and returns two arrays; one where each entry is the sensitivity at a given threshold and the other where entries are 1-specificities. ROC curves are a good way to visualize sensitivity vs. 1-specificity for varying cut off points. "ROC" takes a list containing different *threshold* parameter values to try and returns two arrays; one where each entry is the sensitivity at a given threshold and the other where entries are 1-specificities.
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
# TODO: Programming Assignment 1 # TODO: Programming Assignment 1
def ROC(true_labels, preds, value_list): def ROC(true_labels, preds, value_list):
''' '''
Args: Args:
true_labels: ndarray true_labels: ndarray
1D array containing true labels 1D array containing true labels
preds: ndarray preds: ndarray
1D array containing thresholded value (e.g. proportion of neighbors in kNN) 1D array containing thresholded value (e.g. proportion of neighbors in kNN)
value_list: ndarray value_list: ndarray
1D array containing different threshold values 1D array containing different threshold values
Returns: Returns:
sens: ndarray sens: ndarray
1D array containing sensitivities 1D array containing sensitivities
spec_: ndarray spec_: ndarray
1D array containing 1-specifities 1D array containing 1-specifities
''' '''
# calculate sensitivity, 1-specificity # calculate sensitivity, 1-specificity
# return two arrays # return two arrays
raise NotImplementedError raise NotImplementedError
return sens, spec_ return sens, spec_
``` ```
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