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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Check the input and output dimensionsAs a check that your model is working as expected, test out how it responds to input data. | # test that dimensions are as expected
test_rnn = RNN(input_size=1, output_size=1, hidden_dim=10, n_layers=2)
# generate evenly spaced, test data pts
time_steps = np.linspace(0, np.pi, seq_length)
data = np.sin(time_steps)
data.resize((seq_length, 1))
test_input = torch.Tensor(data).unsqueeze(0) # give it a batch_siz... | Input size: torch.Size([1, 20, 1])
Output size: torch.Size([20, 1])
Hidden state size: torch.Size([2, 1, 10])
| MIT | recurrent-neural-networks/time-series/Simple_RNN.ipynb | johnsonjoseph37/deep-learning-v2-pytorch |
--- Training the RNNNext, we'll instantiate an RNN with some specified hyperparameters. Then train it over a series of steps, and see how it performs. | # decide on hyperparameters
input_size=1
output_size=1
hidden_dim=32
n_layers=1
# instantiate an RNN
rnn = RNN(input_size, output_size, hidden_dim, n_layers)
print(rnn) | RNN(
(rnn): RNN(1, 32, batch_first=True)
(fc): Linear(in_features=32, out_features=1, bias=True)
)
| MIT | recurrent-neural-networks/time-series/Simple_RNN.ipynb | johnsonjoseph37/deep-learning-v2-pytorch |
Loss and OptimizationThis is a regression problem: can we train an RNN to accurately predict the next data point, given a current data point?>* The data points are coordinate values, so to compare a predicted and ground_truth point, we'll use a regression loss: the mean squared error.* It's typical to use an Adam opti... | # MSE loss and Adam optimizer with a learning rate of 0.01
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(rnn.parameters(), lr=0.01) | _____no_output_____ | MIT | recurrent-neural-networks/time-series/Simple_RNN.ipynb | johnsonjoseph37/deep-learning-v2-pytorch |
Defining the training functionThis function takes in an rnn, a number of steps to train for, and returns a trained rnn. This function is also responsible for displaying the loss and the predictions, every so often. Hidden StatePay close attention to the hidden state, here:* Before looping over a batch of training data... | # train the RNN
def train(rnn, n_steps, print_every):
# initialize the hidden state
hidden = None
for batch_i, step in enumerate(range(n_steps)):
# defining the training data
time_steps = np.linspace(step * np.pi, (step+1)*np.pi, seq_length + 1)
data = np.sin(time_st... | C:\Users\johnj\miniconda3\lib\site-packages\torch\autograd\__init__.py:145: UserWarning: CUDA initialization: CUDA driver initialization failed, you might not have a CUDA gpu. (Triggered internally at ..\c10\cuda\CUDAFunctions.cpp:109.)
Variable._execution_engine.run_backward(
| MIT | recurrent-neural-networks/time-series/Simple_RNN.ipynb | johnsonjoseph37/deep-learning-v2-pytorch |
Εστω οτι παρατηρούμε εναν αστέρα στον ουρανό και μετράμε τη ροή φωτονίων. Θεωρώντας ότι η ροή είναι σταθερή με το χρόνο ίση με $F_{\mathtt{true}}$. Παίρνουμε $N$ παρατηρήσεις, μετρώντας τη ροή $F_i$ και το σφάλμα $e_i$. Η ανίχνευση ενός φωτονίου είναι ενα ανεξάρτητο γεγονός που ακολουθεί μια τυχαία κατανομή Poisson. Α... | N=100
F_true=1000.
F=np.random.poisson(F_true*np.ones(N))
e=np.sqrt(F)
plt.errorbar(np.arange(N),F,yerr=e, fmt='ok', ecolor='gray', alpha=0.5)
plt.hlines(np.mean(F),0,N,linestyles='--')
plt.hlines(F_true,0,N)
print np.mean(F),np.mean(F)-F_true,np.std(F)
ax=seaborn.distplot(F,bins=N/3)
xx=np.linspace(F.min(),F.max())
g... | _____no_output_____ | MIT | Untitled.ipynb | Mixpap/astrostatistics |
Η αρχική προσέγγιση μας είναι μέσω της μεγιστοποιήσης της πιθανοφάνειας. Με βάση τα δεδομένωα $D_i=(F_i,e_i)$ μπορούμε να υπολογίσουμε τη πιθανότητα να τα έχουμε παρατηρήσει δεδομένου της αληθινής τιμής $F_{\mathtt{true}}$ υποθέτωντας ότι τα σφάλματα είναι gaussian$$P(D_i|F_{\mathtt{true}})=\frac{1}{\sqrt{2\pi e_i^2}}e... | #xx=np.linspace(0,10,5000)
xx=np.ones(1000)
#seaborn.distplot(np.random.poisson(xx),kde=False)
plt.hist(np.random.poisson(xx))
w = 1. / e ** 2
print("""
F_true = {0}
F_est = {1:.0f} +/- {2:.0f} (based on {3} measurements)
""".format(F_true, (w * F).sum() / w.sum(), w.sum() ** -0.5, N))
np.sum(((F-F.m... |
F_true = 1000.0
F_est = 997 +/- 3 (based on 100 measurements)
| MIT | Untitled.ipynb | Mixpap/astrostatistics |
Load MNIST Data | # MNIST dataset downloaded from Kaggle :
#https://www.kaggle.com/c/digit-recognizer/data
# Functions to read and show images.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
d0 = pd.read_csv('./mnist_train.csv')
print(d0.head(5)) # print first five rows of d0.
# save the labels into a ... | _____no_output_____ | Apache-2.0 | 2.assign_amzn_fine_food_review_tsne/pca_tsne_mnist.ipynb | be-shekhar/learning-ml |
2D Visualization using PCA | # Pick first 15K data-points to work on for time-effeciency.
#Excercise: Perform the same analysis on all of 42K data-points.
labels = l.head(15000)
data = d.head(15000)
print("the shape of sample data = ", data.shape)
# Data-preprocessing: Standardizing the data
from sklearn.preprocessing import StandardScaler
sta... | _____no_output_____ | Apache-2.0 | 2.assign_amzn_fine_food_review_tsne/pca_tsne_mnist.ipynb | be-shekhar/learning-ml |
PCA using Scikit-Learn | # initializing the pca
from sklearn import decomposition
pca = decomposition.PCA()
# configuring the parameteres
# the number of components = 2
pca.n_components = 2
pca_data = pca.fit_transform(sample_data)
# pca_reduced will contain the 2-d projects of simple data
print("shape of pca_reduced.shape = ", pca_data.shap... | _____no_output_____ | Apache-2.0 | 2.assign_amzn_fine_food_review_tsne/pca_tsne_mnist.ipynb | be-shekhar/learning-ml |
PCA for dimensionality redcution (not for visualization) | # PCA for dimensionality redcution (non-visualization)
pca.n_components = 784
pca_data = pca.fit_transform(sample_data)
percentage_var_explained = pca.explained_variance_ / np.sum(pca.explained_variance_);
cum_var_explained = np.cumsum(percentage_var_explained)
# Plot the PCA spectrum
plt.figure(1, figsize=(6, 4))
... | _____no_output_____ | Apache-2.0 | 2.assign_amzn_fine_food_review_tsne/pca_tsne_mnist.ipynb | be-shekhar/learning-ml |
t-SNE using Scikit-Learn | # TSNE
from sklearn.manifold import TSNE
# Picking the top 1000 points as TSNE takes a lot of time for 15K points
data_1000 = standardized_data[0:1000,:]
labels_1000 = labels[0:1000]
model = TSNE(n_components=2, random_state=0)
# configuring the parameteres
# the number of components = 2
# default perplexity = 30
# ... | _____no_output_____ | Apache-2.0 | 2.assign_amzn_fine_food_review_tsne/pca_tsne_mnist.ipynb | be-shekhar/learning-ml |
APIs and data Catherine Devlin (@catherinedevlin)Innovation Specialist, 18FOakwood High School, Feb 16 2017 Who am I?(hint: not Jean Valjean) Cool things I've done- Chemical engineer in college- Oops, beca... | !pip install requests | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
Then, we import. That's like getting it out of the cupboard. | import requests | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
Oakwood High School | with offline.use_cassette('offline.vcr'):
response = requests.get('http://ohs.oakwoodschools.org/pages/Oakwood_High_School')
response.ok
response.status_code
print(response.text) | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
We have backed our semi up to the front door.OK, back to checking out politicians. | url = 'https://api.open.fec.gov/v1/committee/C00373001/totals/?page=1&api_key=DEMO_KEY&sort=-cycle&per_page=20'
with offline.use_cassette('offline.vcr'):
response = requests.get(url)
response.ok
response.status_code
response.json()
response.json()['results']
results = response.json()['results']
results[0]['cycle']... | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
[Pandas](http://pandas.pydata.org/) | !pip install pandas
import pandas as pd
data = pd.DataFrame(response.json()['results'])
data
data = data.set_index('cycle')
data
data['disbursements']
data[data['disbursements'] < 1000000 ] | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
[Bokeh](http://bokeh.pydata.org/en/latest/) | !pip install bokeh
from bokeh.charts import Bar, show, output_notebook
by_year = Bar(data, values='disbursements')
output_notebook()
show(by_year) | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
Playtime[so many options](http://bokeh.pydata.org/en/latest/docs/user_guide/charts.html)- Which column to map?- Colors or styles?- Scatter- Better y-axis label?- Some other candidate committee? - Portman C00458463, Brown C00264697- Filter it Where's it coming from?https://api.open.fec.gov/v1/committee/C00373001/sche... | url = 'https://api.open.fec.gov/v1/committee/C00373001/schedules/schedule_a/by_state/?per_page=20&api_key=DEMO_KEY&page=1&cycle=2016'
with offline.use_cassette('offline.vcr'):
response = requests.get(url)
results = response.json()['results']
data = pd.DataFrame(results)
data
data = data.set_index('state')
by_state ... | _____no_output_____ | CC0-1.0 | presentation_vcr.ipynb | catherinedevlin/code-org-apis-data |
[Diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.htmldiabetes-dataset)---------------- | import pandas as pd
from sklearn import datasets
diabetes = datasets.load_diabetes()
print(diabetes['DESCR'])
# Convert the data to a pandas dataframe
df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
df['diabetes'] = diabetes.target
df.head() | _____no_output_____ | MIT | ejercicios/reg-toy-diabetes.ipynb | joseluisGA/videojuegos |
Random ForestAplicação do random forest em uma mão de poker***Dataset:*** https://archive.ics.uci.edu/ml/datasets/Poker+Hand***Apresentação:*** https://docs.google.com/presentation/d/1zFS4cTf9xwvcVPiCOA-sV_RFx_UeoNX2dTthHkY9Am4/edit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.utils import column_or_1d
from sklearn.linear_model import LogisticRegression
from ... | _____no_output_____ | MIT | RandomForest.ipynb | AM-2018-2-dusteam/ML-poker |
Description This task is to do an exploratory data analysis on the balance-scale dataset Data Set Information This data set was generated to model psychological experimental results. Each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. The attributes are the left w... | #importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
#reading the data
data=pd.read_csv('balance-scale.data')
#shape of the data
data.shape
#first five rows of the data
data.head()
#Generating the x values
x=data.drop(['Class'],axis=1)
x.head()
#Generating the ... | _____no_output_____ | MIT | AnushkaProject/Balance Scale Decision Tree.ipynb | Sakshat682/BalanceDataProject |
Using the Weight and Distance parameters Splitting the data set into a ratio of 70:30 by the built in 'train_test_split' function in sklearn to get a better idea of accuracy of the model | from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x,y,stratify=y, test_size=0.3, random_state=2)
X_train.describe()
#Importing decision tree classifier and creating it's object
from sklearn.tree import DecisionTreeClassifier
clf= DecisionTreeClassifier()
clf.fit(X_... | _____no_output_____ | MIT | AnushkaProject/Balance Scale Decision Tree.ipynb | Sakshat682/BalanceDataProject |
We observe that the accuracy score is pretty low. Thus, we need to find optimal parameters to get the best accuracy. We do that by using GridSearchCV | #Using GridSearchCV to find the maximun optimal depth
from sklearn.model_selection import GridSearchCV
tree_para={"criterion":["gini","entropy"], "max_depth":[3,4,5,6,7,8,9,10,11,12]}
dt_model_grid= GridSearchCV(DecisionTreeClassifier(random_state=3),tree_para, cv=10)
dt_model_grid.fit(X_train,y_train)
# To print the o... | _____no_output_____ | MIT | AnushkaProject/Balance Scale Decision Tree.ipynb | Sakshat682/BalanceDataProject |
Using the created Torque | dt_model2 = DecisionTreeClassifier(random_state=31)
X_train, X_test, y_train, y_test= train_test_split(x1,y, stratify=y, test_size=0.3, random_state=8)
X_train.head(
)
X_train.shape
dt_model2.fit(X_train, y_train)
y_pred2= dt_model2.predict(X_test)
print(classification_report(y_test, y_pred2, target_names=["Balanced","... | _____no_output_____ | MIT | AnushkaProject/Balance Scale Decision Tree.ipynb | Sakshat682/BalanceDataProject |
Increasing the optimization After observing the trees, we conclude that differences are not being taken into account. Hence, we add the differences attribute to try and increase the accuracy. | x1['Diff']= x1['LT']- x1['RT']
x1.head()
X_train, X_test, y_train, y_test =train_test_split(x1,y, stratify=y, test_size=0.3,random_state=40)
dt_model3= DecisionTreeClassifier(random_state=40)
dt_model3.fit(X_train, y_train)
#Create Classification Report
y_pred3= dt_model3.predict(X_test)
print(classification_report(y_t... | _____no_output_____ | MIT | AnushkaProject/Balance Scale Decision Tree.ipynb | Sakshat682/BalanceDataProject |
Final Conclusion The model returns a perfect accuracy score as desired. | !pip install seaborn
| Collecting seaborn
Downloading seaborn-0.11.2-py3-none-any.whl (292 kB)
Requirement already satisfied: numpy>=1.15 in c:\python39\lib\site-packages (from seaborn) (1.21.2)
Requirement already satisfied: scipy>=1.0 in c:\python39\lib\site-packages (from seaborn) (1.7.1)
Requirement already satisfied: matplotlib>=2.2 i... | MIT | AnushkaProject/Balance Scale Decision Tree.ipynb | Sakshat682/BalanceDataProject |
3.10 多层感知机的简洁实现 | import torch
from torch import nn
from torch.nn import init
import numpy as np
import sys
sys.path.append("..")
import d2lzh_pytorch as d2l
print(torch.__version__) | 0.4.1
| Apache-2.0 | code/chapter03_DL-basics/3.10_mlp-pytorch.ipynb | fizzyelf-es/Dive-into-DL-PyTorch |
3.10.1 定义模型 | num_inputs, num_outputs, num_hiddens = 784, 10, 256
net = nn.Sequential(
d2l.FlattenLayer(),
nn.Linear(num_inputs, num_hiddens),
nn.ReLU(),
nn.Linear(num_hiddens, num_outputs),
)
for params in net.parameters():
init.normal_(params, mean=0, std=0.01) | _____no_output_____ | Apache-2.0 | code/chapter03_DL-basics/3.10_mlp-pytorch.ipynb | fizzyelf-es/Dive-into-DL-PyTorch |
3.10.2 读取数据并训练模型 | batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer) | epoch 1, loss 0.0031, train acc 0.703, test acc 0.757
epoch 2, loss 0.0019, train acc 0.824, test acc 0.822
epoch 3, loss 0.0016, train acc 0.845, test acc 0.825
epoch 4, loss 0.0015, train acc 0.855, test acc 0.811
epoch 5, loss 0.0014, train acc 0.865, test acc 0.846
| Apache-2.0 | code/chapter03_DL-basics/3.10_mlp-pytorch.ipynb | fizzyelf-es/Dive-into-DL-PyTorch |
Summarizing Emails using Machine Learning: Data Wrangling Table of Contents1. Imports & Initalization 2. Data Input A. Enron Email Dataset B. BC3 Corpus 3. Preprocessing A. Data Cleaning. B. Sentence Cleaning C. Tokenizing 4. Store Data A. Locally as pickle B. Into database 5. Data Explorat... | import sys
from os import listdir
from os.path import isfile, join
import configparser
from sqlalchemy import create_engine
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import email
import mailparser
import xml.etree.ElementTree as ET
from talon.signature.bruteforce import extract_signature
... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
2. Data Input A. Enron Email DatasetThe raw enron email dataset contains a maildir directory that contains folders seperated by employee which contain the emails. The following processes the raw text of each email into a dask dataframe with the following columns: Employee: The username of the email owner. Body: Clean... | def process_email(index):
'''
This function splits a raw email into constituent parts that can be used as features.
'''
email_path = index[0]
employee = index[1]
folder = index[2]
mail = mailparser.parse_from_file(email_path)
full_body = email.message_from_string(mail.body)
... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
B. BC3 Corpus This dataset is split into two xml files. One contains the original emails split line by line, and the other contains the summarizations created by the annotators. Each email may contain several summarizations from different annotators and summarizations may also be over several emails. This will create ... | def parse_bc3_emails(root):
'''
This adds every BC3 email to a newly created dataframe.
'''
BC3_email_list = []
#The emails are seperated by threads.
for thread in root:
email_num = 0
#Iterate through the thread elements <name, listno, Doc>
for thread_element in thread:
... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
The second dataframe contains the summarizations of each email:Annotator: Person who created summarization. Email_num: Email in thread sequence. Listno: Thread identifier. Summary: Human summarization of the email. | def parse_bc3_summaries(root):
'''
This parses every BC3 Human summary that is contained in the dataset.
'''
BC3_summary_list = []
for thread in root:
#Iterate through the thread elements <listno, name, annotation>
for thread_element in thread:
if thread_element.tag == "... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
3. Preprocessing A. Data Cleaning | #Convert date to pandas datetime.
enron_email_df['date'] = pd.to_datetime(enron_email_df['date'], utc=True)
bc3_df['date'] = pd.to_datetime(bc3_df.date, utc=True)
#Look at the timeframe
start_date = str(enron_email_df.date.min())
end_date = str(enron_email_df.date.max())
print("Start Date: " + start_date)
print("End ... | Start Date: 1980-01-01 00:00:00+00:00
End Date: 2024-05-26 10:49:57+00:00
| MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
Since the Enron data was collected in May 2002 according to wikipedia its a bit strange to see emails past that date. Reading some of the emails seem to suggest it's mostly spam. | enron_email_df[(enron_email_df.date > '2003-01-01')].head()
#Quick look at emails before 1999,
enron_email_df[(enron_email_df.date < '1999-01-01')].date.value_counts().head()
enron_email_df[(enron_email_df.date == '1980-01-01')].head() | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
There seems to be a glut of emails dated exactly on 1980-01-01. The emails seem legitimate, but these should be droped since without the true date we won't be able to figure out where the email fits in the context of a batch of summaries. Keep emails between Jan 1st 1999 and June 1st 2002. | enron_email_df = enron_email_df[(enron_email_df.date > '1998-01-01') & (enron_email_df.date < '2002-06-01')] | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
B. Sentence Cleaning The raw enron email Corpus tends to have a large amount of unneeded characters that can interfere with tokenizaiton. It's best to do a bit more cleaning. | def clean_email_df(df):
'''
These remove symbols and character patterns that don't aid in producing a good summary.
'''
#Removing strings related to attatchments and certain non numerical characters.
patterns = ["\[IMAGE\]","-", "_", "\*", "+","\".\""]
for pattern in patterns:
df['body'... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
C. Tokenizing It's important to split up sentences into it's constituent parts for the ML algorithim that will be used for text summarization. This will aid in further processing like removing extra whitespace. We can also remove stopwords, which are very commonly used words that don't provide additional sentence mean... | def remove_stopwords(sen):
'''
This function removes stopwords
'''
stop_words = stopwords.words('english')
sen_new = " ".join([i for i in sen if i not in stop_words])
return sen_new
def tokenize_email(text):
'''
This function splits up the body into sentence tokens and removes stop word... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
Starting with the Enron dataset. | #This tokenizing will be the extracted sentences that may be chosen to form the email summaries.
enron_email_df['extractive_sentences'] = enron_email_df['body'].apply(sent_tokenize)
#Splitting the text in emails into cleaned sentences
enron_email_df['tokenized_body'] = enron_email_df['body'].apply(tokenize_email)
#Tok... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
Now working on the BC3 Dataset. | bc3_df['extractive_sentences'] = bc3_df['body'].apply(sent_tokenize)
bc3_df['tokenized_body'] = bc3_df['body'].apply(tokenize_email)
#bc3_email_df = bc3_email_df.loc[bc3_email_df.astype(str).drop_duplicates(subset='tokenized_body').index] | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
Store Data A. Locally as pickle After all the preprocessing is finished its best to store the the data so it can be quickly and easily retrieved by other software. Pickles are best used if you are working locally and want a simple way to store and load data. You can also use a cloud database that can be accessed by o... | #Local locations for pickle files.
ENRON_PICKLE_LOC = "../data/dataframes/wrangled_enron_full_df.pkl"
BC3_PICKLE_LOC = "../data/dataframes/wrangled_BC3_df.pkl"
#Store dataframes to disk
enron_email_df.to_pickle(ENRON_PICKLE_LOC)
bc3_df.head()
bc3_df.to_pickle(BC3_PICKLE_LOC) | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
B. Into database I used a Postgres database with the DB configurations stored in a config_notebook.ini file. This allows me to easily switch between local and AWS configurations. | #Configure postgres database
config = configparser.ConfigParser()
config.read('config_notebook.ini')
#database_config = 'LOCAL_POSTGRES'
database_config = 'AWS_POSTGRES'
POSTGRES_ADDRESS = config[database_config]['POSTGRES_ADDRESS']
POSTGRES_USERNAME = config[database_config]['POSTGRES_USERNAME']
POSTGRES_PASSWORD = ... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
5. Data Exploration Exploring the dataset can go a long way to building more accurate machine learning models and spotting any possible issues with the dataset. Since the Enron dataset is quite large, we can speed up some of our computations by using Dask. While not strictly necessary, iterating on this dataset should... | client = Client(processes = True)
client.cluster
#Make into dask dataframe.
enron_email_df = dd.from_pandas(enron_email_df, npartitions=cpus)
enron_email_df.columns
#Used to create a describe summary of the dataset. Ignoring tokenized columns.
enron_email_df[['body', 'chain', 'date', 'email_folder', 'employee', 'from... | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
B. BC3 Corpus | bc3_df.head()
bc3_df['to'].value_counts().head() | _____no_output_____ | MIT | notebooks/Process_Emails.ipynb | dailykirt/ML_Enron_email_summary |
Compass heading | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('Heading [degree]', fontsize=16)
ax1.plot(standardized_time, compass_heading, label='compass heading')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.show... | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
Temperature | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('Temperature [degree]', fontsize=16)
ax1.plot(standardized_time, temp, label='temp', color='k')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.show()
pri... | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
PH | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('PH', fontsize=16)
ax1.plot(standardized_time, PH, label='PH', color='r')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.show()
print("Standard Deviation... | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
Conductivity | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('Conductivity [ms]', fontsize=16)
ax1.plot(standardized_time, cond, label='conductivity', color='b')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.show()... | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
Chlorophyll | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('chlorophyll [RFU]', fontsize=16)
ax1.plot(standardized_time, chlorophyll, label='chlorophyll', color='g')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.... | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
ODO | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('ODO [%sat]', fontsize=16)
ax1.plot(standardized_time, ODO, label='ODO', color='m')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.show()
print("Standard... | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
Sonar depth | # Figure initialization
fig, ax1 = plt.subplots()
ax1.set_xlabel('Time [sec]', fontsize=16)
ax1.set_ylabel('sonar [m]', fontsize=16)
ax1.plot(standardized_time, sonar, label='sonar', color='c')
ax1.legend()
for wp in standardized_time2:
plt.axvline(x=wp, color='gray', linestyle='--')
plt.show() | _____no_output_____ | MIT | Jupyter_notebook/ISER2021/Path 1/.ipynb_checkpoints/20200626-Sunapee-manualvisit-checkpoint.ipynb | dartmouthrobotics/epscor_asv_data_analysis |
Classification Binary classification Stochastic gradient descent (SGD) | from sklearn.linear_model import SGDClassifier | _____no_output_____ | Apache-2.0 | cheat-sheets/ml/classification/algorithms.ipynb | AElOuassouli/reading-notes |
QSVM multiclass classificationA [multiclass extension](https://qiskit.org/documentation/apidoc/qiskit.aqua.components.multiclass_extensions.html) works in conjunction with an underlying binary (two class) classifier to provide classification where the number of classes is greater than two.Currently the following multi... | import numpy as np
from qiskit import BasicAer
from qiskit.circuit.library import ZZFeatureMap
from qiskit.utils import QuantumInstance, algorithm_globals
from qiskit_machine_learning.algorithms import QSVM
from qiskit_machine_learning.multiclass_extensions import AllPairs
from qiskit_machine_learning.utils.dataset_he... | _____no_output_____ | Apache-2.0 | tutorials/02_qsvm_multiclass.ipynb | gabrieleagl/qiskit-machine-learning |
We want a dataset with more than two classes, so here we choose the `Wine` dataset that has 3 classes. | from qiskit_machine_learning.datasets import wine
n = 2 # dimension of each data point
sample_Total, training_input, test_input, class_labels = wine(training_size=24,
test_size=6, n=n, plot_data=True)
temp = [test_input[k] for k in test_input]
total_array ... | _____no_output_____ | Apache-2.0 | tutorials/02_qsvm_multiclass.ipynb | gabrieleagl/qiskit-machine-learning |
To used a multiclass extension an instance thereof simply needs to be supplied, on the QSVM creation, using the `multiclass_extension` parameter. Although `AllPairs()` is used in the example below, the following multiclass extensions would also work: OneAgainstRest() ErrorCorrectingCode(code_size=5) | algorithm_globals.random_seed = 10598
backend = BasicAer.get_backend('qasm_simulator')
feature_map = ZZFeatureMap(feature_dimension=get_feature_dimension(training_input),
reps=2, entanglement='linear')
svm = QSVM(feature_map, training_input, test_input, total_array,
multiclass_ext... | _____no_output_____ | Apache-2.0 | tutorials/02_qsvm_multiclass.ipynb | gabrieleagl/qiskit-machine-learning |
Building Simple Neural NetworksIn this section you will:* Import the MNIST dataset from Keras.* Format the data so it can be used by a Sequential model with Dense layers.* Split the dataset into training and test sections data.* Build a simple neural network using Keras Sequential model and Dense layers.* Train that m... | # For drawing the MNIST digits as well as plots to help us evaluate performance we
# will make extensive use of matplotlib
from matplotlib import pyplot as plt
# All of the Keras datasets are in keras.datasets
from keras.datasets import mnist
# Keras has already split the data into training and test data
(training_im... | _____no_output_____ | Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
Problems With This DataThere are (at least) two problems with this data as it is currently formatted, what do you think they are? 1. The input data is formatted as a 2D array, but our deep neural network needs to data as a 1D vector. * This is because of how deep neural networks are constructed, it is simply not poss... | from keras.utils import to_categorical
# Preparing the dataset
# Setup train and test splits
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
# 28 x 28 = 784, because that's the dimensions of the MNIST data.
image_size = 784
# Reshaping the training_images and test_images to lists ... | [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
| Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
Building a Deep Neural NetworkNow that we've prepared our data, it's time to build a simple neural network. To start we'll make a deep network with 3 layers—the input layer, a single hidden layer, and the output layer. In a deep neural network all the layers are 1 dimensional. The input layer has to be the shape of ou... | from keras.models import Sequential
from keras.layers import Dense
# Sequential models are a series of layers applied linearly.
model = Sequential()
# The first layer must specify it's input_shape.
# This is how the first two layers are added, the input layer and the hidden layer.
model.add(Dense(units=32, activation... | Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 32) 25120
__________________________________... | Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
Compiling and Training a ModelOur model must be compiled and trained before it can make useful predictions. Models are trainined with the training data and training labels. During this process Keras will use an optimizer, loss function, metrics of our chosing to repeatedly make predictions and recieve corrections. The... | # sgd stands for stochastic gradient descent.
# categorical_crossentropy is a common loss function used for categorical classification.
# accuracy is the percent of predictions that were correct.
model.compile(optimizer="sgd", loss='categorical_crossentropy', metrics=['accuracy'])
# The network will make predictions f... | Train on 54000 samples, validate on 6000 samples
Epoch 1/5
54000/54000 [==============================] - 1s 17us/step - loss: 1.3324 - accuracy: 0.6583 - val_loss: 0.8772 - val_accuracy: 0.8407
Epoch 2/5
54000/54000 [==============================] - 1s 13us/step - loss: 0.7999 - accuracy: 0.8356 - val_loss: 0.6273 - ... | Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
Evaluating Our ModelNow that we've trained our model, we want to evaluate its performance. We're using the "test data" here although in a serious experiment, we would likely not have done nearly enough work to warrent the application of the test data. Instead, we would rely on the validation metrics as a proxy for our... | loss, accuracy = model.evaluate(test_data, test_labels, verbose=True)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['training', 'validation'], loc='best')
plt.show()
plt.plot(history.history['loss'])... | 10000/10000 [==============================] - 0s 15us/step
| Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
How Did Our Network Do? * Why do we only have one value for test loss and test accuracy, but a chart over time for training and validation loss and accuracy?* Our model was more accurate on the validation data than it was on the training data. * Is this okay? Why or why not? * What if our model had been more accura... | from numpy import argmax
# Predicting once, then we can use these repeatedly in the next cell without recomputing the predictions.
predictions = model.predict(test_data)
# For pagination & style in second cell
page = 0
fontdict = {'color': 'black'}
# Repeatedly running this cell will page through the predictions
for ... | _____no_output_____ | Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
Will A Different Network Perform Better?Given what you know so far, use Keras to build and train another sequential model that you think will perform __better__ than the network we just built and trained. Then evaluate that model and compare its performance to our model. Remember to look at accuracy and loss for train... | # Your code here...
| _____no_output_____ | Unlicense | 01-intro-to-deep-learning/02-building-simple-neural-networks.ipynb | rekil156/intro-to-deep-learning |
> The email portion of this campaign was actually run as an A/B test. Half the emails sent out were generic upsells to your product while the other half contained personalized messaging around the users’ usage of the site.这是 AB Test 的实验内容。 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# export
'''Calculate conversion rates and related metrics.'''
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
def conversion_rate(dataframe, column_names, converted = 'converted', id_name = 'user_id'):
'''Calculate conv... | _____no_output_____ | MIT | 01-demo1.ipynb | JiaxiangBU/conversion_metrics |
差异不大。 | # Group marketing by user_id and variant
subscribers = email.groupby(['user_id',
'variant'])['converted'].max()
subscribers_df = pd.DataFrame(subscribers.unstack(level=1))
# Drop missing values from the control column
control = subscribers_df['control'].dropna()
# Drop missing values fr... | Control conversion rate: 0.2814814814814815
Personalization conversion rate: 0.3908450704225352
| MIT | 01-demo1.ipynb | JiaxiangBU/conversion_metrics |
这种 Python 写法我觉得有点复杂。 $$\begin{array}{l}{\text { Calculating lift: }} \\ {\qquad \frac{\text { Treatment conversion rate - Control conversion rate }}{\text { Control conversion rate }}}\end{array}$$ 注意这里的 lift 是转化率的比较，因此是可以超过 100 % | # export
def lift(a,b, sig = 2):
'''Calculate lift statistic for an AB test.
Cite https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas
Parmaters
---------
a: float.
control group.
b: float.
test group.
sig: integer.
default 2.
Returns
-... | _____no_output_____ | MIT | 01-demo1.ipynb | JiaxiangBU/conversion_metrics |
查看是否统计显著 | # export
from scipy import stats
def lift_sig(a,b):
'''Calculate lift statistical significance for an AB test.
Cite https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas
Parmaters
---------
a: float.
control group.
b: float.
test group.
sig: integer.
... | The t value of the two variables is -0.577 with p value 0.580
| MIT | 01-demo1.ipynb | JiaxiangBU/conversion_metrics |
> In the next lesson, you will explore whether that holds up across all demographics.这真是做 AB test 一个成熟的思维，不代表每一个 group 都很好。 | # export
def ab_test(df, segment, id_name = 'user_id', test_column = 'variant', converted = 'converted'):
'''Calculate lift statistic by segmentation.
Cite https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas
Parmaters
---------
df: pandas.DataFrame.
segment: str.
... | _____no_output_____ | MIT | 01-demo1.ipynb | JiaxiangBU/conversion_metrics |
Ran the new few blocks for my colab configuration, can be ignored. | from google.colab import drive
drive.mount('/content/gdrive')
!wget https://d17h27t6h515a5.cloudfront.net/topher/2016/December/584f6edd_data/data.zip
import shutil
shutil.move("/content/data.zip", "/content/gdrive/My Drive/udacity-behavioural-cloning/")
os.chdir('/content/gdrive/My Drive/udacity-behavioural-cloning/... | _____no_output_____ | MIT | behavioral-cloning/model.ipynb | KOKSANG/Self-Driving-Car |
Training code starts here | df = pd.read_csv('driving_log.csv')
# Visualizing original distribution
plt.figure(figsize=(15, 3))
hist, bins = np.histogram(df.steering.values, bins=50)
plt.hist(df.steering.values, bins=bins)
plt.title('Steering Distribution Plot')
plt.xlabel('Steering')
plt.ylabel('Count')
plt.show()
# create grayscale image
def g... | _____no_output_____ | MIT | behavioral-cloning/model.ipynb | KOKSANG/Self-Driving-Car |
entities-search-engine loadingSPARQL query to `{"type": [values]}` | import sys
sys.path.append("..")
from heritageconnector.config import config
from heritageconnector.utils.sparql import get_sparql_results
from heritageconnector.utils.wikidata import url_to_qid
import json
import time
from tqdm import tqdm
endpoint = config.WIKIDATA_SPARQL_ENDPOINT | _____no_output_____ | MIT | experiments/entities-search-engine/1. load data from sparql.ipynb | TheScienceMuseum/heritage-connector |
humans sample | limit = 10000
query = f"""
SELECT ?item WHERE {{
?item wdt:P31 wd:Q5.
}} LIMIT {limit}
"""
res = get_sparql_results(endpoint, query)
data = {
"humans": [url_to_qid(x['item']['value']) for x in res['results']['bindings']]
}
with open("./entities-search-engine/data/humans_sample.json", 'w') as f:
json.dump(... | _____no_output_____ | MIT | experiments/entities-search-engine/1. load data from sparql.ipynb | TheScienceMuseum/heritage-connector |
humans sample: paginatedgot a 500 timeout error nearly all of the way through. Looked like it was going to take around 1h20m. *Better to do with dump?* | # there are 8,011,382 humans in Wikidata so this should take 161 iterations
total_humans = 8011382
pagesize = 40000
reslen = pagesize
paged_json = []
i = 0
start = time.time()
pbar = tqdm(total=total_humans)
while reslen == pagesize:
query = f"""
SELECT ?item WHERE {{
?item wdt:P31 wd:Q5.
}} LIMIT ... |
0it [00:00, ?it/s][A
3it [00:00, 24.90it/s][A
5it [00:00, 22.01it/s][A
8it [00:00, 23.76it/s][A
11it [00:00, 24.37it/s][A
15it [00:00, 24.48it/s][A
19it [00:00, 26.02it/s][A
22it [00:00, 26.84it/s][A
25it [00:00, 27.48it/s][A
29it [00:01, 28.67it/s][A
32it [00:01, 27.39it/s][A
35it [00:01, 27.23it/s][A
38i... | MIT | experiments/entities-search-engine/1. load data from sparql.ipynb | TheScienceMuseum/heritage-connector |
By now basically everyone ([here](http://datacolada.org/2014/06/04/23-ceiling-effects-and-replications/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+DataColada+%28Data+Colada+Feed%29), [here](http://yorl.tumblr.com/post/87428392426/ceiling-effects), [here](http://www.talyarkoni.org/blog/2014/06/01/there-i... | %pylab inline
import pystan
from matustools.matusplotlib import *
from scipy import stats
il=['dog','trolley','wallet','plane','resume','kitten','mean score','median score']
D=np.loadtxt('schnallstudy1.csv',delimiter=',')
D[:,1]=1-D[:,1]
Dtemp=np.zeros((D.shape[0],D.shape[1]+1))
Dtemp[:,:-1]=D
Dtemp[:,-1]=np.median(D[:... | /usr/local/lib/python2.7/dist-packages/matplotlib-1.3.1-py2.7-linux-i686.egg/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Arial'] not found. Falling back to Bitstream Vera Sans
(prop.get_family(), self.defaultFamily[fontext]))
/usr/local/lib/python2.7/dist-packages/matplotlib-1.3.1-py2.7-linu... | MIT | _ipynb/SchnallSupplement.ipynb | simkovic/simkovic.github.io |
Legend: OC - original study, control group; OT - original study, treatment group; RC - replication study, control group; RT - replication study, treatment group; In the original study the difference between the treatment and control is significantly greater than zero. In the replication, it is not. However the ratings ... | def plotComparison(A,B,stan=False):
plt.figure(figsize=(8,16))
cl=['control','treatment']
x=np.arange(11)-0.5
if not stan:assert A.shape[1]==B.shape[1]
for i in range(A.shape[1]-1):
for cond in range(2):
plt.subplot(A.shape[1]-1,2,2*i+cond+1)
a=np.histogram(A[A[:,0]==... | _____no_output_____ | MIT | _ipynb/SchnallSupplement.ipynb | simkovic/simkovic.github.io |
!pip3 install xgboost > /dev/null
import pandas as pd
import numpy as np
import io
import gc
import time
from pprint import pprint
# import PIL.Image as Image
# import matplotlib.pylab as plt
from datetime import date
# import tensorflow as tf
# import tensorflow_hub as hub
# settings
import warnings
warnings.filterw... | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects | |
Removing NaN in target variable | # select rows where amount is not NaN
df_train = df_train[df_train['Amount'].notna()]
df_train[df_train['Amount'].isna()].shape
# delete rows where Amount < 0
df_train = df_train[df_train['Amount'] >= 0]
df_train[['Cost_of_vehicle', 'Min_coverage', 'Max_coverage', 'Amount']].describe()
selected_columns = ['Cost_of_vehi... | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Checking if the dataset is balanced/imbalanced - Condition | # python check if dataset is imbalanced : https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets
target_count = df_train['Condition'].value_counts()
print('Class 0 (No):', target_count[0])
print('Class 1 (Yes):', target_count[1])
print('Proportion:', round(target_count[0] / target_count[1], 2), ':... | Class 0 (No): 99
Class 1 (Yes): 1288
Proportion: 0.08 : 1
| MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Splitting Data into train-cv | classification_labels = df_train['Condition'].values
# for regresion delete rows where Condition = 0
df_train_regression = df_train[df_train['Condition'] == 1]
regression_labels = df_train_regression['Amount'].values
######
df_train_regression.drop(['Condition','Amount'], axis=1, inplace=True)
df_train.drop(['Condit... | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Over Sampling using SMOTE | # https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/
from imblearn.over_sampling import SMOTE
smote_overSampling = SMOTE()
X_train,y_train = smote_overSampling.fit_resample(X_train,y_train)
unique, counts = np.unique(y_train, return_counts=True)
dict(zip(unique, counts)) | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Scaling data | from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_cv_scaled = scaler.transform(X_cv)
X_test_scaled = scaler.transform(df_test)
X_train_scaled | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Modelling & Cross-Validation Classification | %%time
# Train multiple models : https://www.kaggle.com/tflare/testing-multiple-models-with-scikit-learn-0-79425
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ense... | {
"AdaBoost": {
"f1": 0.9991397849462367
}
}
| MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Regression | X_train_regression, X_cv_regression, y_train_regression, y_cv_regression = train_test_split(df_train_regression, regression_labels, test_size=0.1)
scaler = StandardScaler()
X_train_scaled_regression = scaler.fit_transform(X_train_regression)
X_cv_scaled_regression = scaler.transform(X_cv_regression)
X_test_scaled_reg... | Best: 0.062463 using {}
| MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Predicting on CV data | classification_alg = AdaBoost
# regression_alg = ExtraTreesReg
# hypertuned model
regression_alg = gsc
classification_alg.fit(X_train_scaled, y_train)
regression_alg.fit(X_train_scaled_regression, y_train_regression)
# predictions_class = classification_alg.predict(X_cv)
# pprint(classification_alg.get_params())
# ... | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Predicting on test Data | trained_classifier = classification_alg
trained_regressor = regression_alg
predictions_trained_classifier_test = trained_classifier.predict(X_test_scaled)
predictions_trained_regressor_test = trained_regressor.predict(X_test_scaled_regression)
read = pd.read_csv(gDrivePath + 'test.csv')
submission = pd.DataFrame({
... | _____no_output_____ | MIT | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects |
Build a Traffic Sign Recognition Classifier Deep Learning Some improvements are taken :- [x] Adding of convolution networks at the same size of previous layer, to get 1x1 layer- [x] Activation function use 'ReLU' instead of 'tanh'- [x] Adaptative learning rate, so learning rate is decayed along to training phase- [x] ... | # load enhanced traffic signs
import os
import cv2
import matplotlib.pyplot as plot
import numpy
dir_enhancedsign = 'figures\enhanced_training_dataset2'
files_enhancedsign = [os.path.join(dir_enhancedsign, f) for f in os.listdir(dir_enhancedsign)]
# read & resize (32,32) images in enhanced dataset
images_enhance... | [0, 1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 6, 7, 8, 9]
| MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
*Enhanced German traffic signs dataset &8595;* **We would have 50 classes in total with new enhanced training dataset :** | n_classes_enhanced = len(numpy.unique(y_enhancedsign))
print('n_classes enhanced : {}'.format(n_classes_enhanced)) | n_classes enhanced : 50
| MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Load and Visualize the standard German Traffic Signs Dataset | # Load pickled data
import pickle
import numpy
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'traffic-signs-data/train.p'
validation_file = 'traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'
with open(training_file, mode='rb') as f:
train = ... | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Implementation of LeNet>http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf Above is the original article of Pierre Sermanet and Yann LeCun in 1998 that we can follow to create LeNet convolutional networks with a good accuracy even for very-beginners in deep-learning. It's really excited to see that many yea... | ### Import tensorflow and keras
import tensorflow as tf
from tensorflow import keras
print ("TensorFlow version: " + tf.__version__) | TensorFlow version: 2.1.0
| MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
2-stage ConvNet architecture by Pierre Sermanet and Yann LeCunWe will try to implement the 2-stage ConvNet architecture by Pierre Sermanet and Yann LeCun which is not sequential. Keras disposes keras.Sequential() API for sequential architectures but it can not handle models with non-linear topology, shared layers or m... | #LeNet model
inputs = keras.Input(shape=(32,32,3), name='image_in')
#0 stage :conversion from normalized RGB [0..1] to HSV
layer_HSV = tf.image.rgb_to_hsv(inputs)
#1st stage ___________________________________________________________
#Convolution with ReLU activation
layer1_conv = keras.layers.C... | Model: "LeNet_Model_improved"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
============================================================================================... | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Input preprocessing Color-SpacePierre Sermanet and Yann LeCun used YUV color space with almost of processings on Y-channel (Y stands for brightness, U and V stand for Chrominance). NormalizationEach channel of an image is in uint8 scale (0-255), we will normalize each channel to 0-1. Generally, we normalize data to... | import cv2
def input_normalization(X_in):
X = numpy.float32(X_in/255.0)
return X
# normalization of dataset
# enhanced training dataset is added
X_train_norm = input_normalization(X_train)
X_valid_norm = input_normalization(X_valid)
X_enhancedtrain_norm = input_normalization(images... | (50, 32, 32, 3)
1
0
| MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Training Pipeline_Optimizer : we use Adam optimizer, better than SDG (Stochastic Gradient Descent) _Loss function : Cross Entropy by category _Metrics : accuracy *learning rate 0.001 work well with our network, it's better to try with small laerning rate in the begining. | rate = 0.001
LeNet_Model.compile(
optimizer=keras.optimizers.Nadam(learning_rate = rate, beta_1=0.9, beta_2=0.999, epsilon=1e-07),
loss=keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=["accuracy"]) | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Real-time data augmentation | from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen_enhanced = ImageDataGenerator(
rotation_range=30.0,
zoom_range=0.5,
width_shift_range=0.5,
height_shift_range=0.5,
featurewise_center=True,
... | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Train the Model on standard training dataset | EPOCHS = 30
BATCH_SIZE = 32
STEPS_PER_EPOCH = int(len(X_train_norm)/BATCH_SIZE)
history_standard_HLS = LeNet_Model.fit(
datagen.flow(X_train_norm, y_train_onehot, batch_size=BATCH_SIZE,shuffle=True),
validation_data=(X_valid_norm, y_valid_onehot),
shuffle=T... | WARNING:tensorflow:sample_weight modes were coerced from
...
to
['...']
Train for 1087 steps, validate on 4410 samples
Epoch 1/30
1087/1087 [==============================] - 310s 285ms/step - loss: 1.5436 - accuracy: 0.5221 - val_loss: 1.1918 - val_accuracy: 0.6120
Epoch 2/30
1087/1087 [=====================... | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
on enhanced training dataset | EPOCHS = 30
BATCH_SIZE = 1
STEPS_PER_EPOCH = int(len(X_enhancedtrain_norm)/BATCH_SIZE)
history_enhanced_HLS = LeNet_Model.fit(
datagen_enhanced.flow(X_enhancedtrain_norm, y_enhanced_onehot, batch_size=BATCH_SIZE,shuffle=True),
shuffle=True, #validat... | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Evaluate the ModelWe will use the test dataset to evaluate classification accuracy. | #Normalize test dataset
X_test_norm = input_normalization(X_test)
#One-hot matrix
y_test_onehot = keras.utils.to_categorical(y_test, n_classes)
#Load saved model
reconstructed_LeNet_Model = keras.models.load_model("LeNet_enhanced_trainingdataset_HLS.h5")
#Evaluate and display the prediction
result ... |
Plot of training accuracy over 30 epochs:
| MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Prediction of test dataset with trained modelWe will use the test dataset to test trained model's prediction of instances that it has never seen during training. | print("Test Set : {} samples".format(len(X_test)))
print('n_classes : {}'.format(n_classes))
X_test.shape
#Normalize test dataset
X_test_norm = input_normalization(X_test)
#One-hot matrix
y_test_onehot = keras.utils.to_categorical(y_test, n_classes)
#Load saved model
reconstructed = keras.models.load_model... | Image 0 - Target = 16, Predicted = 6
Image 1 - Target = 1, Predicted = 6
Image 2 - Target = 38, Predicted = 6
Image 3 - Target = 33, Predicted = 6
Image 4 - Target = 11, Predicted = 6
Image 5 - Target = 38, Predicted = 6
Image 6 - Target = 18, Predicted = 6
Image 7 - Target = 12, Predicted = 6
Image 8 - Target = 25, Pr... | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
We will display a confusion matrix on test dataset to figure out our error-rate. `X_test_norm` : test dataset `y_test` : test dataset ground truth labels `y_pred_class` : prediction labels on test dataset | confusion_matrix = numpy.zeros([n_classes, n_classes]) | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
confusion_matrix`column` : test dataset ground truth labels `row` : prediction labels on test dataset `diagonal` : incremented when prediction matches ground truth label | for ij in range(len(X_test_norm)):
if y_test[ij] == y_pred_class[ij]:
confusion_matrix[y_test[ij],y_test[ij]] += 1
else:
confusion_matrix[y_pred_class[ij],y_test[ij]] -= 1
column_label = [' L % d' % x for x in range(n_classes)]
row_label = [' P % d' % x for x in range(n_classes)]
# Pl... | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
Thank to confusion matrix, we could identify where to enhance -[x] training dataset -[x] real-time data augmentation -[x] preprocessing *Extract of confusion matrix of classification on test dataset &8595;* Prediction of new instances with trained modelWe will use the test dataset to test trained model's predic... | # load french traffic signs
import os
import cv2
import matplotlib.pyplot as plot
import numpy
dir_frenchsign = 'french_traffic-signs-data'
images_frenchsign = [os.path.join(dir_frenchsign, f) for f in os.listdir(dir_frenchsign)]
images_frenchsign = numpy.array([cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2RGB) for ... | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
*Enhanced German traffic signs dataset &8595;* | # manually label for these new images
y_frenchsign = [13, 31, 29, 24, 26, 27, 33, 17, 15, 34, 12, 2, 2, 4, 2]
n_classes = n_classes_enhanced
# when a sign doesn't present in our training dataset, we'll try to find a enough 'similar' sign to label it.
# image 2 : class 29 differed
# image 3 : class 24,... | _____no_output_____ | MIT | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow |
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