markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Let's visualize our regression function with the scatterplot showing the original data set. Herefore, we use the predicted values. | #visualize data points
plt.scatter(df1.x, df1.y, color = "y", marker = "o", s = 40)
#visualize regression function
plt.plot(descriptiveFeatures1, targetFeature1_predict, color = "g")
plt.xlabel('x')
plt.ylabel('y')
plt.title('the data and the regression function')
plt.show() | _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Now it is your turn. Build a simple linear regression for the data below. Use col1 as descriptive feature and col2 as target feature. Also plot your results. | df2 = pd.DataFrame({'col1': [770, 677, 428, 410, 371, 504, 1136, 695, 551, 550], 'col2': [54, 47, 28, 38, 29, 38, 80, 52, 45, 40]})
#Your turn
| _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
EvaluationUsually, the model and its predictions is not sufficient. In the following we want to evaluate our classifiers. Let's start by computing their error. The sklearn.metrics package contains several errors such as* Mean squared error* Mean absolute error* Mean squared log error* Median absolute error | #computing the squared error of the first model
print("Mean squared error model 1: %.2f" % mean_squared_error(targetFeature1, targetFeature1_predict)) | Mean squared error model 1: 0.56
| Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
We can also visualize the errors: | plt.scatter(targetFeature1_predict, (targetFeature1 - targetFeature1_predict) ** 2, color = "blue", s = 10,)
## plotting line to visualize zero error
plt.hlines(y = 0, xmin = 0, xmax = 15, linewidth = 2)
## plot title
plt.title("Squared errors Model 1")
## function to show plot
plt.show() | _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Now it is your turn. Compute the mean squared error and visualize the squared errors. Play around using different error metrics. | #Your turn
| _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Handling multiple descriptive features at once - Multiple linear regressionIn most cases, we will have more than one descriptive feature . As an example we use an example data set of the scikit package. The dataset describes housing prices in Boston based on several attributes. Note, in this format the data is already... | from sklearn import datasets ## imports datasets from scikit-learn
df3 = datasets.load_boston()
#The sklearn package provides the data splitted into a set of descriptive features and a target feature.
#We can easily transform this format into the pandas data frame as used above.
descriptiveFeatures3 = pd.DataFrame(df3... | Descriptive features:
CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \
0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0
1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0
2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0
3 0.0... | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
To predict the housing price we will use a Multiple Linear Regression model. In Python this is very straightforward: we use the same function as for simple linear regression, but our set of descriptive features now contains more than one element (see above). | classifier = LinearRegression()
model3 = classifier.fit(descriptiveFeatures3,targetFeature3)
targetFeature3_predict = classifier.predict(descriptiveFeatures3)
print('Coefficients: \n', classifier.coef_)
print('Intercept: \n', classifier.intercept_)
print("Mean squared error: %.2f" % mean_squared_error(targetFeature3, ... | Coefficients:
[[-1.08011358e-01 4.64204584e-02 2.05586264e-02 2.68673382e+00
-1.77666112e+01 3.80986521e+00 6.92224640e-04 -1.47556685e+00
3.06049479e-01 -1.23345939e-02 -9.52747232e-01 9.31168327e-03
-5.24758378e-01]]
Intercept:
[36.45948839]
Mean squared error: 21.89
| Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
As you can see above, we have a coefficient for each descriptive feature. Handling categorical descriptive featuresSo far we always encountered numerical dscriptive features, but data sets can also contain categorical attributes. The regression function can only handle numerical input. There are several ways to tranfo... | #example using pandas
df4 = pd.DataFrame({'A':['a','b','c'],'B':['c','b','a'] })
one_hot_pd = pd.get_dummies(df4)
one_hot_pd
#example using scikit
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
#apply the one hot encoder
encoder = OneHotEncoder(categories='auto')
encoder.fit(df4)
df4_OneHot = encoder.tr... | Transformed by One-hot Encoding:
[[1. 0. 0. 0. 0. 1.]
[0. 1. 0. 0. 1. 0.]
[0. 0. 1. 1. 0. 0.]]
Replacing categories by numerical labels:
A B
0 0 2
1 1 1
2 2 0
| Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Now it is your turn. Perform linear regression using the data set given below. Don't forget to transform your categorical descriptive features. The rental price attribute represents the target variable. | df5 = pd.DataFrame({'Size':[500,550,620,630,665],'Floor':[4,7,9,5,8], 'Energy rating':['C', 'A', 'A', 'B', 'C'], 'Rental price': [320,380,400,390,385] })
#Your turn
| _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Predicting a categorical target value - Logistic regression We might also encounter data sets where our target feature is categorical. Here we don't transform them into numerical values, but insetad we use a logistic regression function. Luckily, sklearn provides us with a suitable function that is similar to the line... | # Importing the dataset
iris = pd.read_csv('iris.csv')
print('First look at the data set: ')
print(iris.head())
#defining the descriptive and target features
descriptiveFeatures_iris = iris[['sepal_length']] #we only use the attribute 'sepal_length' in this example
targetFeature_iris = iris['species'] #we want to pre... | First look at the data set:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 ... | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Now it is your turn. In the example above we only used the first attribute as descriptive variable. Change the example such that all available attributes are used. | #Your turn | _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Note, that the regression classifier (both logistic and non-logistic) can be tweaked using several parameters. This includes, but is not limited to, non-linear regression. Check out the documentation for details and feel free to play around! Support Vector Machines Aside from regression models, the sklearn package als... | from sklearn.svm import SVC
#define descriptive and target features as before
descriptiveFeatures_iris = iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
targetFeature_iris = iris['species']
#this time, we train an SVM classifier
classifier = SVC(C=1, kernel='linear', gamma = 'auto')
classifier.fi... | _____no_output_____ | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
As explained in the lecture, a support vector machine is defined by its support vectors. In the sklearn package we can access them and their properties very easily:* support_: indicies of support vectors* support_vectors_: the support vectors* n_support_: the number of support vectors for each class | print('Indicies of support vectors:')
print(classifier.support_)
print('The support vectors:')
print(classifier.support_vectors_)
print('The number of support vectors for each class:')
print(classifier.n_support_) | Indicies of support vectors:
[ 23 24 41 52 56 63 66 68 70 72 76 77 83 84 98 106 110 119
123 126 127 129 133 138 146 147 149]
The support vectors:
[[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[4.5 2.3 1.3 0.3]
[6.9 3.1 4.9 1.5]
[6.3 3.3 4.7 1.6]
[6.1 2.9 4.7 1.4]
[5.6 3. 4.5 1.5]
[6.2 2.2 4.5 1.5]
[5.9 3... | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
We can also calculate the distance of the data points to the separating hyperplane by using the decision_function(X) method. Score(X,y) calculates the mean accuracy of the classification. The classification report shows metrics such as precision, recall, f1-score and support. You will learn more about these quality met... | from sklearn.metrics import classification_report
classifier.decision_function(descriptiveFeatures_iris)
print('Accuracy: \n', classifier.score(descriptiveFeatures_iris,targetFeature_iris))
print('Classification report: \n')
print(classification_report(targetFeature_iris, targetFeature_iris_predict)) | Accuracy:
0.9933333333333333
Classification report:
precision recall f1-score support
setosa 1.00 1.00 1.00 50
versicolor 1.00 0.98 0.99 50
virginica 0.98 1.00 0.99 50
accuracy 0.99 ... | Apache-2.0 | IDS_2020/RegressionSVM/Instruction4-RegressionSVM-without solutions.ipynb | pmnatos/DataScience |
Grade distrubtion by Faculties (used in the appendix) | # note: no NaN's
print(df['Fakultet'].unique())
# Number of courses by faculty
print(sum(df['Fakultet'] == 'Det Natur- og Biovidenskabelige Fakultet'))
print(sum(df['Fakultet'] == 'Det Samfundsvidenskabelige Fakultet'))
print(sum(df['Fakultet'] == 'Det Humanistiske Fakultet'))
print(sum(df['Fakultet'] == 'Det Sundhedsv... | _____no_output_____ | MIT | Exam project/Jacob legemappe/ANALYSIS_JENS_CLEAN.ipynb | tnv875/Group18-NoTeeth |
Grade distrubtion by Faculties, weighted against ECTS | # We need to weight the grades according to ECTS points. If we do not small courses will have the same weight as
# bigger courses.
df['Weigthed_m3'] = df['Credit_edit'] * df[-3]
df['Weigthed_00'] = df['Credit_edit'] * df[0]
df['Weigthed_02'] = df['Credit_edit'] * df[2]
df['Weigthed_4'] = df['Credit_edit'] * df[4]
df['... | _____no_output_____ | MIT | Exam project/Jacob legemappe/ANALYSIS_JENS_CLEAN.ipynb | tnv875/Group18-NoTeeth |
Faculties, weighted against ECTS - droppping -3 and 00 | y_no_fail_ects = []
x_no_fail = ['02','4','7','10','12']
# Looking at each faculty
for i in tqdm_notebook(df['Fakultet'].unique()):
df_faculty = df[df['Fakultet']==i]
y_no_fail_ects_inner=[]
for k in ['Weigthed_02','Weigthed_4','Weigthed_7','Weigthed_10','Weigthed_12']:
y_no_fail_ects_inn... | _____no_output_____ | MIT | Exam project/Jacob legemappe/ANALYSIS_JENS_CLEAN.ipynb | tnv875/Group18-NoTeeth |
Calculating GPA by faculty | from math import isnan
import math
#Calculate gpa when ONLY PASSED exams are counted
snit = []
for i in range(0,len(df)):
x_02 = df[2][i]
x_04 = df[4][i]
x_07 = df[7][i]
x_10 = df[10][i]
x_12 = df[12][i]
number = (x_12,x_10,x_07,x_04,x_02)
grades = [12,10,7,4,2]
mydick = dict(zip(gr... | _____no_output_____ | MIT | Exam project/Jacob legemappe/ANALYSIS_JENS_CLEAN.ipynb | tnv875/Group18-NoTeeth |
Type of assessments broken down by faculties | list_type_ass=list(df['Type of assessmet_edit'].unique())
y_type_ass_inner = []
y_type_ass = []
# Looking at each faculty
for i in tqdm_notebook(df['Fakultet'].unique()):
df_faculty = df[df['Fakultet']==i]
# Running through each type of assessment.
for k in list_type_ass:
# Summing number of ... | _____no_output_____ | MIT | Exam project/Jacob legemappe/ANALYSIS_JENS_CLEAN.ipynb | tnv875/Group18-NoTeeth |
Step1. Import and Load Data | !pip install -q pip install git+https://github.com/huggingface/transformers.git
!pip install -q datasets
from datasets import load_dataset
emotions = load_dataset("emotion")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | _____no_output_____ | Apache-2.0 | SentimentalAnalysisWithGPTNeo.ipynb | bhadreshpsavani/ExploringSentimentalAnalysis |
Step2. Preprocess Data | from transformers import AutoTokenizer
model_name = "EleutherAI/gpt-neo-125M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
def tokenize(batch):
return tokenizer(batch["text"], padding=True, truncation=True)
tokenizer
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
tokenizer
emotions_encoded = emot... | _____no_output_____ | Apache-2.0 | SentimentalAnalysisWithGPTNeo.ipynb | bhadreshpsavani/ExploringSentimentalAnalysis |
parameters | CLUSTER_ALGO = 'KMedoids'
C_SHAPE ='circle'
#C_SHAPE ='CIRCLE'
#C_SHAPE ='ellipse'
#N_CLUSTERS = [50,300, 1000]
N_CLUSTERS = [3]
CLUSTERS_STD = 0.3
N_P_CLUSTERS = [3, 30, 300, 3000]
N_CLUSTERS_S = N_CLUSTERS[0]
INNER_FOLDS = 3
OUTER_FOLDS = 3 | _____no_output_____ | MIT | Cluster/kmeans-kmedoids/KMedoids-blobs-3-0.3-varypts.ipynb | bcottman/photon_experiments |
includes | %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_iris
from sklearn.datasets import make_blobs
from sklearn.datasets import make_moons
%load_ext autoreload
%autoreload 2
packages = !conda list
packages | _____no_output_____ | MIT | Cluster/kmeans-kmedoids/KMedoids-blobs-3-0.3-varypts.ipynb | bcottman/photon_experiments |
Output registry | from __future__ import print_function
import sys, os
old__file__ = !pwd
__file__ = !cd ../../../photon ;pwd
#__file__ = !pwd
__file__ = __file__[0]
__file__
sys.path.append(__file__)
print(sys.path)
os.chdir(old__file__[0])
!pwd
old__file__[0]
import seaborn as sns; sns.set() # for plot styling
import numpy as np
im... | /opt/conda/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading p... | MIT | Cluster/kmeans-kmedoids/KMedoids-blobs-3-0.3-varypts.ipynb | bcottman/photon_experiments |
function defintions | def yield_parameters_ellipse(n_p_clusters):
cluster_std = CLUSTERS_STD
for n_p_cluster in n_p_clusters:
for n_cluster in N_CLUSTERS:
print('ncluster:', n_cluster)
n_cluster_std = [cluster_std for k in range(n_cluster)]
n_samples = [n_p_cluster for k in range(n_cluster... |
PhotonCore
ARDRegression sklearn.linear_model.ARDRegression Estimator
AdaBoostClassifier sklearn.ensemble.AdaBoostClassifier Estimator
AdaBoostRegressor sklearn.ensemble.AdaBoostReg... | MIT | Cluster/kmeans-kmedoids/KMedoids-blobs-3-0.3-varypts.ipynb | bcottman/photon_experiments |
KMeans blobs |
registry.info(CLUSTER_ALGO)
def hyper_cluster(cluster_name):
if C_SHAPE == 'ellipse' :
yield_cluster = yield_parameters_ellipse
else:
yield_cluster = yield_parameters
n_p_clusters = N_P_CLUSTERS
for data_X, data_y,n_cluster in yield_cluster(n_p_clusters):
simple_output('C... | _____no_output_____ | MIT | Cluster/kmeans-kmedoids/KMedoids-blobs-3-0.3-varypts.ipynb | bcottman/photon_experiments |
[NTDS'19] assignment 1: network science[ntds'19]: https://github.com/mdeff/ntds_2019[Eda Bayram](https://lts4.epfl.ch/bayram), [EPFL LTS4](https://lts4.epfl.ch) and[Nikolaos Karalias](https://people.epfl.ch/nikolaos.karalias), [EPFL LTS2](https://lts2.epfl.ch). Students* Team: ``* `<Alice Bizeul, Gaia Carparelli, An... | import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Question 1: Construct a Citation Graph and a Feature Graph Read the `cora.content` file into a Pandas DataFrame by setting a header for the column names. Check the `README` file. | column_list = ['paper_id'] + [str(i) for i in range(1,1434)] + ['class_label']
pd_content = pd.read_csv('data/cora/cora.content', delimiter='\t', names=column_list)
pd_content.head() | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Print out the number of papers contained in each of the reasearch fields.**Hint:** You can use the `value_counts()` function. | pd_content['class_label'].value_counts() | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Select all papers from a field of your choice and store their feature vectors into a NumPy array.Check its shape. | my_field = 'Neural_Networks'
features = pd_content[pd_content['class_label'] == my_field].drop(columns=['paper_id','class_label']).to_numpy()
features.shape | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Let $D$ be the Euclidean distance matrix whose $(i,j)$ entry corresponds to the Euclidean distance between feature vectors $i$ and $j$.Using the feature vectors of the papers from the field which you have selected, construct $D$ as a Numpy array. | distance = np.zeros([features.shape[0],features.shape[0]])
for i in range(features.shape[0]):
distance[i] = np.sqrt(np.sum((features[i,:] - features)**2, axis=1))
distance.shape | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the mean pairwise distance $\mathbb{E}[D]$. | # Mean on the upper triangle as the matrix is symetric (we also excluded the diagonal)
mean_distance = distance[np.triu_indices(distance.shape[1],1)].mean()
print('Mean euclidian distance between feature vectors of papers on Neural Networks: {}'.format(mean_distance)) | Mean euclidian distance between feature vectors of papers on Neural Networks: 5.696602496555962
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Plot an histogram of the euclidean distances. | fig,ax = plt.subplots(1,1,figsize=(8, 8))
ax.hist(distance.flatten(), density=True, bins=20, color='salmon', edgecolor='black', linewidth=1);
ax.set_title("Histogram of Euclidean distances between Neural-networks papers")
ax.set_xlabel("Euclidian Distances")
ax.set_ylabel("Frequency")
ax.grid(True, which='major', axis=... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Now create an adjacency matrix for the papers by thresholding the Euclidean distance matrix.The resulting (unweighted) adjacency matrix should have entries$$ A_{ij} = \begin{cases} 1, \; \text{if} \; d(i,j)< \mathbb{E}[D], \; i \neq j, \\ 0, \; \text{otherwise.} \end{cases} $$First, let us choose the mean distance as t... | threshold = mean_distance
A_feature = np.where(distance < threshold, 1, 0)
np.fill_diagonal(A_feature,0) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Now read the `cora.cites` file and construct the citation graph by converting the given citation connections into an adjacency matrix. | cora_cites = np.genfromtxt('data/cora/cora.cites', delimiter='\t')
papers = np.unique(cora_cites)
A_citation = np.zeros([papers.size, papers.size])
for i in range(cora_cites.shape[0]):
A_citation[np.where(papers==cora_cites[i,1]),np.where(papers==cora_cites[i,0])] = 1
A_citation.shape | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Get the adjacency matrix of the citation graph for the field that you chose.You have to appropriately reduce the adjacency matrix of the citation graph. | # get the paper id from the chosen field
field_id = pd_content[pd_content['class_label'] == my_field]["paper_id"].unique()
# get the index of those paper in the A_citation matrix (similar to index on the vector 'papers')
field_citation_id = np.empty(field_id.shape[0]).astype(int)
for i in range(field_id.shape[0]):
... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check if your adjacency matrix is symmetric. Symmetrize your final adjacency matrix if it's not already symmetric. | # a matrix is symetric if it's the same as its transpose
print('The citation adjency matrix for papers on Neural Networks is symmetric: {}'.format(np.all(A_citation == A_citation.transpose())))
# symetrize it by taking the maximum between A and A.transposed
A_citation = np.maximum(A_citation, A_citation.transpose())
#... | The citation adjency matrix for papers on Neural Networks is symmetric: False
After modifiying the matrix, it is symmetric: True
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the shape of your adjacency matrix again. | A_citation.shape | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Question 2: Degree Distribution and Moments What is the total number of edges in each graph? | num_edges_feature = int(np.sum(A_feature)/2) # only half of the matrix
num_edges_citation = int(np.sum(A_citation)/2)
print(f"Number of edges in the feature graph: {num_edges_feature}")
print(f"Number of edges in the citation graph: {num_edges_citation}") | Number of edges in the feature graph: 136771
Number of edges in the citation graph: 1175
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Plot the degree distribution histogram for each of the graphs. | degrees_citation = A_citation.sum(axis=1) # degree = nbr of connections --> sum of ones over columns (axis=1)
degrees_feature = A_feature.sum(axis=1)
deg_hist_normalization = np.ones(degrees_citation.shape[0]) / degrees_citation.shape[0]
fig, axes = plt.subplots(1, 2, figsize=(16, 8))
axes[0].set_title('Citation gra... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Calculate the first and second moments of the degree distribution of each graph. | cit_moment_1 = np.mean(degrees_citation)
cit_moment_2 = np.var(degrees_citation)
feat_moment_1 = np.mean(degrees_feature)
feat_moment_2 = np.var(degrees_feature)
print(f"1st moment of citation graph: {cit_moment_1:.3f}")
print(f"2nd moment of citation graph: {cit_moment_2:.3f}")
print(f"1st moment of feature graph: {... | 1st moment of citation graph: 2.873
2nd moment of citation graph: 15.512
1st moment of feature graph: 334.403
2nd moment of feature graph: 55375.549
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
What information do the moments provide you about the graphs?Explain the differences in moments between graphs by comparing their degree distributions. Answer :**The moments provide an idea of the sparsity of the graphs and the way the data is distributed using numerical values. The first moment is associated with the... | smallest_feat_hub_idx = np.argpartition(degrees_feature, degrees_feature.shape[0]-20)[:-20]
smallest_feat_hub_idx.sort()
reduced_A_feature = A_feature[smallest_feat_hub_idx][:,smallest_feat_hub_idx]
smallest_cit_hub_idx = np.argpartition(degrees_citation, degrees_citation.shape[0]-20)[:-20]
smallest_cit_hub_idx.sort()... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Plot the new degree distribution histograms. | reduced_degrees_feat = reduced_A_feature.sum(axis=1)
reduced_degrees_cit = reduced_A_citation.sum(axis=1)
deg_hist_normalization = np.ones(reduced_degrees_feat.shape[0])/reduced_degrees_feat.shape[0]
fig, axes = plt.subplots(1, 2, figsize=(16, 8))
axes[0].set_title('Citation graph degree distribution')
axes[0].hist(re... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Compute the first and second moments for the new graphs. | reduced_cit_moment_1 = np.mean(reduced_degrees_cit)
reduced_cit_moment_2 = np.var(reduced_degrees_cit)
reduced_feat_moment_1 = np.mean(reduced_degrees_feat)
reduced_feat_moment_2 = np.var(reduced_degrees_feat)
print(f"Citation graph first moment: {reduced_cit_moment_1:.3f}")
print(f"Citation graph second moment: {red... | Citation graph first moment: 1.972
Citation graph second moment: 2.380
Feature graph first moment: 302.308
Feature graph second moment: 50780.035
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Print the number of edges in the reduced graphs. | num_edges_reduced_feature = int(np.sum(reduced_A_feature)/2)
num_edges_reduced_citation = int(np.sum(reduced_A_citation)/2)
print(f"Number of edges in the reduced feature graph: {num_edges_reduced_feature}")
print(f"Number of edges in the reduced citation graph: {num_edges_reduced_citation}") | Number of edges in the reduced feature graph: 120621
Number of edges in the reduced citation graph: 787
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Is the effect of removing the hubs the same for both networks? Look at the percentage changes for each moment. Which of the moments is affected the most and in which graph? Explain why. **Hint:** Examine the degree distributions. | change_cit_moment_1 = (reduced_cit_moment_1-cit_moment_1)/cit_moment_1
change_cit_moment_2 = (reduced_cit_moment_2-cit_moment_2)/cit_moment_2
change_feat_moment_1 = (reduced_feat_moment_1-feat_moment_1)/feat_moment_1
change_feat_moment_2 = (reduced_feat_moment_2-feat_moment_2)/feat_moment_2
print(f"% Percentage of cha... | % Percentage of change for citation 1st moment: -31.343
% Percentage of change for citation 2nd moment: -84.656
% Percentage of change for feature 1st moment: -9.598
% Percentage of change for feature 2nd moment: -8.299
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Answer : **After looking of the percentage of change of moments, we can notice that the removal of the 20 largest hubs affects way more the citation degree distribution than the feature degree distribution. The 2nd moment of the citation degree distribution is reduced by almost 85%, this can be due to the fact that th... | threshold = np.max(distance)
diagonal = distance.shape[0]
threshold_flag = False
epsilon = 0.01*threshold
tolerance = 250
while threshold > 0 and not threshold_flag:
threshold -= epsilon # steps of 1% of maximum
n_edge = int((np.count_nonzero(np.where(distance < threshold, 1, 0)) - diagonal)/2)
# wit... | Found a threshold : 2.957
Number of edges in the feature graph: 136771
Number of edges in the feature graph after pruning: 1386
Number of edges in the citation graph: 1175
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Remark:**The distribution of distances (which is a distribution of integers) for this particular field (Neural Networks) doesn't allow a configuration where the number of edges is roughly close (whithin a hundred of edges) to the citation distribution. This is independant of the chosen epsilon . The closest match is 2... | fig, axes = plt.subplots(1, 2, figsize=(12, 6))
axes[0].set_title('Citation graph sparsity')
axes[0].spy(A_citation);
axes[1].set_title('Feature graph sparsity')
axes[1].spy(A_feature_pruned); | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Let $C_{k}(i,j)$ denote the number of paths of length $k$ from node $i$ to node $j$. We define the path matrix $P$, with entries$ P_{ij} = \displaystyle\sum_{k=0}^{N}C_{k}(i,j). $ Calculate the path matrices for both the citation and the unpruned feature graphs for $N =10$. **Hint:** Use [powers of the adjacency matri... | def path_matrix(A, N=10):
"""Compute the path matrix for matrix A for N power """
power_A = [A]
for i in range(N-1):
power_A.append(np.matmul(power_A[-1], A))
return np.stack(power_A, axis=2).sum(axis=2)
path_matrix_citation = path_matrix(A_citation)
path_matrix_feature = path_matrix(A_feature) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the sparsity pattern for both of path matrices. | fig, axes = plt.subplots(1, 2, figsize=(16, 9))
axes[0].set_title('Citation Path matrix sparsity')
axes[0].spy(path_matrix_citation);
axes[1].set_title('Feature Path matrix sparsity')
axes[1].spy(path_matrix_feature, vmin=0, vmax=1); #scaling the color bar | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Now calculate the path matrix of the pruned feature graph for $N=10$. Plot the corresponding sparsity pattern. Is there any difference? | path_matrix_pruned = path_matrix(A_feature_pruned)
plt.figure(figsize=(12, 6))
plt.title('Feature Path matrix sparsity')
plt.spy(path_matrix_pruned); | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Your answer here: **Many combinations of nodes have a path matrix value of zero now, meaning that they are not within the reach of N = 10 nodes from another node. This makes sense as many edges were removed in the pruning procedure (from 136000 to 1400). Hence, the number of possible paths from i to j was reduced, red... | N=0
diameter = None
d_found = False
while not d_found:
N += 1
P = path_matrix(A_feature, N)
if np.count_nonzero(P == 0) == 0: # if there are no zero in P
d_found = True
diameter = N
print(f"The diameter of the feature graph (which is connected) is: {diameter}") | The diameter of the feature graph (which is connected) is: 2
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check if your guess was correct using [NetworkX](https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.distance_measures.diameter.html).Note: usage of NetworkX is only allowed in this part of Section 1. | import networkx as nx
feature_graph = nx.from_numpy_matrix(A_feature)
print(f"Diameter of feature graph according to networkx: {nx.diameter(feature_graph)}") | Diameter of feature graph according to networkx: 2
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Section 2: Network Models In this section, you will analyze the feature and citation graphs you constructed in the previous section in terms of the network model types.For this purpose, you can use the NetworkX libary imported below. | import networkx as nx | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Let us create NetworkX graph objects from the adjacency matrices computed in the previous section. | G_citation = nx.from_numpy_matrix(A_citation)
print('Number of nodes: {}, Number of edges: {}'. format(G_citation.number_of_nodes(), G_citation.number_of_edges()))
print('Number of self-loops: {}, Number of connected components: {}'. format(G_citation.number_of_selfloops(), nx.number_connected_components(G_citation))) | Number of nodes: 818, Number of edges: 1175
Number of self-loops: 0, Number of connected components: 104
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
In the rest of this assignment, we will consider the pruned feature graph as the feature network. | G_feature = nx.from_numpy_matrix(A_feature_pruned)
print('Number of nodes: {}, Number of edges: {}'. format(G_feature.number_of_nodes(), G_feature.number_of_edges()))
print('Number of self-loops: {}, Number of connected components: {}'. format(G_feature.number_of_selfloops(), nx.number_connected_components(G_feature))) | Number of nodes: 818, Number of edges: 1386
Number of self-loops: 0, Number of connected components: 684
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Question 4: Simulation with Erdős–Rényi and Barabási–Albert models Create an Erdős–Rényi and a Barabási–Albert graph using NetworkX to simulate the citation graph and the feature graph you have. When choosing parameters for the networks, take into account the number of vertices and edges of the original networks. The ... | assert len(G_citation.nodes()) == len(G_feature.nodes())
n = len(G_citation.nodes())
print('The number of nodes ({}) matches the original number of nodes: {}'.format(n,n==A_citation.shape[0])) | The number of nodes (818) matches the original number of nodes: True
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
The number of match shall fit the average of the number of edges in the citation and the feature graph. | m = np.round((G_citation.size() + G_feature.size()) / 2)
print('The number of match ({}) fits the average number of edges: {}'.format(m,m==np.round(np.mean([num_edges_citation,num_edges_feature_pruned])))) | The number of match (1280.0) fits the average number of edges: True
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
How do you determine the probability parameter for the Erdős–Rényi graph? Answer:**Based on the principles ruling random networks (no preferential attachment but a random attachment), the expected number of edges is given by : $\langle L \rangle = p\frac{N(N-1)}{2}$ , where $\langle L \rangle$ is the average number o... | p = m*2/(n*(n-1))
G_er = nx.erdos_renyi_graph(n, p) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the number of edges in the Erdős–Rényi graph. | print('My Erdos-Rényi network that simulates the citation graph has {} edges.'.format(G_er.size())) | My Erdos-Rényi network that simulates the citation graph has 1238 edges.
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
How do you determine the preferential attachment parameter for Barabási–Albert graphs? Answer :**The Barabasi-Albert model uses growth and preferential attachement to build a scale-free network. The network is constructed by progressivly adding nodes to the network and adding a fixed number of edges, q, to each node a... | q = int(m/n)
G_ba = nx.barabasi_albert_graph(n, q) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the number of edges in the Barabási–Albert graph. | print('My Barabási-Albert network that simulates the citation graph has {} edges.'.format(G_ba.size())) | My Barabási-Albert network that simulates the citation graph has 817 edges.
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Question 5: Giant Component Check the size of the largest connected component in the citation and feature graphs. | giant_citation = max(nx.connected_component_subgraphs(G_citation), key=len)
print('The giant component of the citation graph has {} nodes and {} edges.'.format(giant_citation.number_of_nodes(), giant_citation.size()))
giant_feature = max(nx.connected_component_subgraphs(G_feature), key=len)
print('The giant component o... | The giant component of the feature graph has 117 nodes and 1364 edges.
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the size of the giant components in the generated Erdős–Rényi graph. | giant_er = max(nx.connected_component_subgraphs(G_er), key=len)
print('The giant component of the Erdos-Rényi network has {} nodes and {} edges.'.format(giant_er.number_of_nodes(), giant_er.size())) | The giant component of the Erdos-Rényi network has 771 nodes and 1234 edges.
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Let us match the number of nodes in the giant component of the feature graph by simulating a new Erdős–Rényi network.How do you choose the probability parameter this time? **Hint:** Recall the expected giant component size from the lectures. Answer :** We can see the average degree of a network/each node as being the ... | GC_node = giant_feature.number_of_nodes()
S = GC_node/n
avg_k = -1/S*np.log(1-S)
p_new = avg_k/(n-1)
G_er_new = nx.erdos_renyi_graph(n, p_new) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Check the size of the new Erdős–Rényi network and its giant component. | print('My new Erdos Renyi network that simulates the citation graph has {} edges.'.format(G_er_new.size()))
giant_er_new = max(nx.connected_component_subgraphs(G_er_new), key=len)
print('The giant component of the new Erdos-Rényi network has {} nodes and {} edges.'.format(giant_er_new.number_of_nodes(), giant_er_new.si... | My new Erdos Renyi network that simulates the citation graph has 437 edges.
The giant component of the new Erdos-Rényi network has 208 nodes and 210 edges.
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Question 6: Degree Distributions Recall the degree distribution of the citation and the feature graph. | fig, axes = plt.subplots(1, 2, figsize=(15, 6),sharex = True)
axes[0].set_title('Citation graph')
citation_degrees = [deg for (node, deg) in G_citation.degree()]
axes[0].hist(citation_degrees, bins=20, color='salmon', edgecolor='black', linewidth=1);
axes[1].set_title('Feature graph')
feature_degrees = [deg for (node,... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
What does the degree distribution tell us about a network? Can you make a prediction on the network model type of the citation and the feature graph by looking at their degree distributions? Answer : **The degree distribution tell us about the sparsity of a network.Both show a power law degree distribution (many nodes... | fig, axes = plt.subplots(1, 3, figsize=(20, 8))
axes[0].set_title('Erdos-Rényi network')
er_degrees = [deg for (node, deg) in G_er.degree()]
axes[0].hist(er_degrees, bins=10, color='salmon', edgecolor='black', linewidth=1)
axes[1].set_title('Barabási-Albert network')
ba_degrees = [deg for (node, deg) in G_ba.degree()... | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
In terms of the degree distribution, is there a good match between the citation and feature graphs and the simulated networks?For the citation graph, choose one of the simulated networks above that match its degree distribution best. Indicate your preference below. Answer : **Regarding the feature network, none of the... | G_config = nx.configuration_model(feature_degrees)
print('Configuration model has {} nodes and {} edges.'.format(G_config.number_of_nodes(), G_config.size())) | Configuration model has 818 nodes and 1386 edges.
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Does it mean that we create the same graph with the feature graph by the configuration model? If not, how do you understand that they are not the same? Answer : **No we don't create the same graph, the number of edges, nodes and degree distribution can be the same but the links can be different. For example, in a grou... | nx.average_clustering(G_citation)
nx.average_clustering(G_feature) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
What does the clustering coefficient tell us about a network? Comment on the values you obtain for the citation and feature graph. Answer :**Clustering coefficient is linked to the presence of subgroups (or clusters) in the network. A high clustering coefficient means that a node is very likely to be part of a subgrou... | nx.average_clustering(G_er)
nx.average_clustering(G_ba)
nx.average_clustering(nx.Graph(G_config)) | _____no_output_____ | Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Comment on the values you obtain for the simulated networks. Is there any good match to the citation or feature graph in terms of clustering coefficient? Answer : **No, there is not any match. The clustering coefficients are rather small compared to the ones for feature and citation graphs. Random networks have genera... | k = m*2/n
p = (nx.average_clustering(G_citation) + nx.average_clustering(G_feature))*0.8
G_pwc = nx.powerlaw_cluster_graph(n, int(k), p)
print('Power law cluster model has {} edges.'.format(G_pwc.size()))
print('Power law cluster model has a clustering coefficient of {}'.format(nx.average_clustering(G_pwc)))
print('C... | Power law cluster model has 2440 edges.
Power law cluster model has a clustering coefficient of 0.16384898371644135
Citation model has 1175 edges.
Citation model has a clustering coefficient of 0.21693567980632222
Feature model has 1386 edges.
Feature model has a clustering coefficient of 0.1220744470334593
| Apache-2.0 | Assignments/1_network_science.ipynb | carparel/NTDS |
Implementing a CGAN for the Iris data set to generate synthetic data Import necessary modules and packages | import os
while os.path.basename(os.getcwd()) != 'Synthetic_Data_GAN_Capstone':
os.chdir('..')
from utils.utils import *
safe_mkdir('experiments')
from utils.data_loading import load_raw_dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.op... | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Set random seed for reproducibility | manualSeed = 999
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed) | Random Seed: 999
| MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Import and briefly inspect data | iris = load_raw_dataset('iris')
iris.head() | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Preprocessing dataSplit 50-50 so we can demonstrate the effectiveness of additional data | x_train, x_test, y_train, y_test = train_test_split(iris.drop(columns='species'), iris.species, test_size=0.5, stratify=iris.species, random_state=manualSeed)
print("x_train:", x_train.shape)
print("x_test:", x_test.shape) | x_train: (75, 4)
x_test: (75, 4)
| MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Model parameters (feel free to play with these) | nz = 32 # Size of generator noise input
H = 16 # Size of hidden network layer
out_dim = x_train.shape[1] # Size of output
bs = x_train.shape[0] # Full data set
nc = 3 # 3 different types of label in this problem
num_batches = 1
num_epochs = 10000
exp_name = 'experiments/iris_1x16'
safe_mkdir(exp_name) | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Adam optimizer hyperparametersI set these based on the original paper, but feel free to play with them as well. | lr = 2e-4
beta1 = 0.5
beta2 = 0.999 | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Set the device | device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu") | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Scale continuous inputs for neural networks | scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_train_tensor = torch.tensor(x_train, dtype=torch.float)
y_train_dummies = pd.get_dummies(y_train)
y_train_dummies_tensor = torch.tensor(y_train_dummies.values, dtype=torch.float) | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Instantiate nets | netG = CGAN_Generator(nz=nz, H=H, out_dim=out_dim, nc=nc, bs=bs, lr=lr, beta1=beta1, beta2=beta2).to(device)
netD = CGAN_Discriminator(H=H, out_dim=out_dim, nc=nc, lr=lr, beta1=beta1, beta2=beta2).to(device) | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Print modelsI chose to avoid using sequential mode in case I wanted to create non-sequential networks, it is more flexible in my opinion, but does not print out as nicely | print(netG)
print(netD) | CGAN_Generator(
(fc1): Linear(in_features=35, out_features=16, bias=True)
(output): Linear(in_features=16, out_features=4, bias=True)
(act): LeakyReLU(negative_slope=0.2)
(loss_fn): BCELoss()
)
CGAN_Discriminator(
(fc1): Linear(in_features=7, out_features=16, bias=True)
(output): Linear(in_features=16, out_... | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Define labels | real_label = 1
fake_label = 0 | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Training LoopLook through the comments to better understand the steps that are taking place | print("Starting Training Loop...")
for epoch in range(num_epochs):
for i in range(num_batches): # Only one batch per epoch since our data is horrifically small
# Update Discriminator
# All real batch first
real_data = x_train_tensor.to(device) # Format batch (entire data set in this case)
... | Starting Training Loop...
[1/10000] Loss_D: 1.3938 Loss_G: 0.7742 D(x): 0.4603 D(G(z)): 0.4609 / 0.4611
[1001/10000] Loss_D: 1.3487 Loss_G: 0.7206 D(x): 0.5089 D(G(z)): 0.4883 / 0.4882
[2001/10000] Loss_D: 1.3557 Loss_G: 0.7212 D(x): 0.5025 D(G(z)): 0.4864 / 0.4864
[3001/10000] Loss_D: 1.4118 Loss_G: 0.6679 D(x): 0.502... | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Output diagnostic plots tracking training progress and statistics | %matplotlib inline
training_plots(netD=netD, netG=netG, num_epochs=num_epochs, save=exp_name)
plot_layer_scatters(netG, title="Generator", save=exp_name)
plot_layer_scatters(netD, title="Discriminator", save=exp_name) | /home/aj/.local/lib/python3.7/site-packages/matplotlib/figure.py:445: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.
% get_backend())
| MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
It looks like training stabilized fairly quickly, after only a few thousand iterations. The fact that the weight norm increased over time probably means that this network would benefit from some regularization. Compare performance of training on fake data versus real dataIn this next section, we will lightly tune two ... | y_test_dummies = pd.get_dummies(y_test)
print("Dummy columns match?", all(y_train_dummies.columns == y_test_dummies.columns))
x_test = scaler.transform(x_test)
labels_list = [x for x in y_train_dummies.columns]
param_grid = {'tol': [1e-9, 1e-8, 1e-7, 1e-6, 1e-5],
'C': [0.5, 0.75, 1, 1.25],
'... | Dummy columns match? True
| MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Train on real data | model_real, score_real = train_test_logistic_reg(x_train, y_train, x_test, y_test, param_grid=param_grid, cv=5, random_state=manualSeed, labels=labels_list) | Accuracy: 0.92
Best Parameters: {'C': 1.25, 'l1_ratio': 0.5, 'tol': 1e-09}
precision recall f1-score support
Iris-setosa 1.00 1.00 1.00 25
Iris-versicolor 0.85 0.92 0.88 25
Iris-virginica 0.91 0.84 0.87 25
accuracy... | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Train on various levels of fake data | test_range = [75, 150, 300, 600, 1200]
fake_bs = bs
fake_models = []
fake_scores = []
for size in test_range:
num_batches = size // fake_bs + 1
genned_data = np.empty((0, out_dim))
genned_labels = np.empty(0)
rem = size
while rem > 0:
curr_size = min(fake_bs, rem)
noise = torch.randn... | For size of: 75
Accuracy: 0.92
Best Parameters: {'C': 0.5, 'l1_ratio': 0.25, 'tol': 1e-09}
precision recall f1-score support
Iris-setosa 1.00 1.00 1.00 25
Iris-versicolor 0.88 0.88 0.88 25
Iris-virginica 0.88 0.88 0.88 25
... | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Well, it looks like this experiment was a success. The models trained on fake data were actually able to outperform models trained on real data, which supports the belief that the CGAN is able to understand the distribution of the data it was trained on and generate meaningful examples that can be used to add additiona... | iris_plot_scatters(genned_data, genned_labels, "Fake Data", scaler, alpha=0.5, save=exp_name) # Fake data
iris_plot_scatters(iris.drop(columns='species'), np.array(iris.species), "Full Real Data Set", alpha=0.5, save=exp_name) # All real data
iris_plot_densities(genned_data, genned_labels, "Fake Data", scaler, save=e... | _____no_output_____ | MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Finally, I present a summary of the test results ran above | fake_data_training_plots(real_range=bs, score_real=score_real, test_range=test_range, fake_scores=fake_scores, save=exp_name) | /home/aj/.local/lib/python3.7/site-packages/matplotlib/figure.py:445: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.
% get_backend())
| MIT | notebooks/reports/iris.ipynb | Atrus619/CSDGAN |
Predictive Maintenance using Machine Learning on Sagemaker*Part 3 - Timeseries data preparation* Initialization---Directory structure to run this notebook:```nasa-turbofan-rul-lstm|+--- data| || +--- interim: intermediate data we can manipulate and process| || \--- raw: *immutable* data downloaded from the so... | %load_ext autoreload
import matplotlib.pyplot as plt
import sagemaker
import boto3
import os
import errno
import pandas as pd
import numpy as np
import seaborn as sns
import json
import sys
import s3fs
import mxnet as mx
import joblib
%matplotlib inline
%autoreload 2
sns.set_style('darkgrid')
sys.path.append('../src'... | _____no_output_____ | MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Loading data from the previous notebook | # Load data from the notebook local storage:
%store -r reduced_train_data
%store -r reduced_test_data
# If the data are not present in the notebook local storage, we need to load them from disk:
success_msg = 'Loaded "reduced_train_data"'
if 'reduced_train_data' not in locals():
print('Nothing in notebook store, t... | (13096, 26)
| MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Study parameters | sequence_length = 20 | _____no_output_____ | MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Normalization--- Normalizing the training dataFirst, we build some scalers based on the training data: | from sklearn import preprocessing
# Isolate the columns to normalize:
normalized_cols = reduced_train_data.columns.difference(['true_rul', 'piecewise_rul'])
# Build MinMax scalers for the features and the labels:
features_scaler = preprocessing.MinMaxScaler()
labels_scaler = preprocessing.MinMaxScaler()
# Normalize ... | (20631, 19)
| MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Normalizing the testing dataNext, we apply these normalizers to the testing data: | normalized_test_data = pd.DataFrame(
features_scaler.transform(reduced_test_data[normalized_cols]),
columns=normalized_cols,
index=reduced_test_data.index
)
reduced_test_data['piecewise_rul'] = reduced_test_data['piecewise_rul'].astype(np.float64)
normalized_test_labels = pd.DataFrame(
labels_scaler.t... | (13096, 26)
| MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Sequences generation--- | from lstm_utils import generate_sequences, generate_labels, generate_training_sequences, generate_testing_sequences
# Building features, target and engine unit lists:
features = normalized_train_data.columns.difference(['true_rul', 'piecewise_rul'])
target = ['piecewise_rul']
unit_list = list(set(normalized_train_data... | Unit 1 test sequence ignored, not enough data points.
Unit 22 test sequence ignored, not enough data points.
Unit 39 test sequence ignored, not enough data points.
Unit 85 test sequence ignored, not enough data points.
train_sequences: (18631, 20, 17)
train_labels: (18631, 1)
test_sequences: (11035, 20, 17)
test_labels... | MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Visualizing the sequencesLet's visualize the sequences we built for an engine (e.g. unit 3 in the example below) to understand what will be fed to the LSTM model: | from lstm_utils import plot_timestep, plot_text
# We use the normalized sequences for the plot but the original data for the label (RUL) for understanding purpose:
current_unit = 1
tmp_sequences = generate_training_sequences(normalized_train_data, sequence_length, features, [current_unit])
tmp_labels = generate_traini... | Sequences generated for unit 1:
| MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Cleanup--- Storing data for the next notebook | %store labels_scaler
%store test_sequences
%store test_labels
%store unit_span
#columns = normalized_train_data.columns.tolist()
#%store columns
#%store train_sequences
#%store train_labels
#%store normalized_train_data
#%store normalized_test_data
#%store sequence_length | Stored 'labels_scaler' (MinMaxScaler)
Stored 'test_sequences' (ndarray)
Stored 'test_labels' (ndarray)
Stored 'unit_span' (list)
| MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Persisting these data to diskThis is useful in case you want to be able to execute each notebook independantly (from one session to another) and don't want to reexecute every notebooks whenever you want to focus on a particular step. Let's start by persisting train and test sequences and the associated labels: | import h5py as h5
train_data = os.path.join(PROCESSED_DATA, 'train.h5')
with h5.File(train_data, 'w') as ftrain:
ftrain.create_dataset('train_sequences', data=train_sequences)
ftrain.create_dataset('train_labels', data=train_labels)
ftrain.close()
test_data = os.path.join(PROCESSED_DATA, 'test.h5')
with h... | _____no_output_____ | MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
Pushing these files to S3: | sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()
prefix = 'nasa-rul-lstm/data'
train_data_location = 's3://{}/{}'.format(bucket, prefix)
s3_resource = boto3.Session().resource('s3')
s3_resource.Bucket(bucket).Object('{}/train/train.h5'.format(prefix)).upload_file(train_data)
s3_resou... | Stored 'data_channels' (dict)
| MIT-0 | nasa-turbofan-rul-lstm/notebooks/3 - Data preparation.ipynb | michaelhoarau/sagemaker-predictive-maintenance |
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