Code from chapter: 10_yodaΒΆ

Code snippet 126:

# inside of DataLad-101
datalad create -c yoda --dataset . midterm_project

Code snippet 127:

cd midterm_project
# we are in midterm_project, thus -d . points to the root of it.
datalad clone -d . https://github.com/datalad-handbook/iris_data.git input/

Code snippet 128:

cd ../
tree -d
cd midterm_project

Code snippet 129:

cat << EOT > code/script.py

import pandas as pd
import seaborn as sns
import datalad.api as dl
from sklearn import model_selection
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report

data = "input/iris.csv"

# make sure that the data are obtained (get will also install linked sub-ds!):
dl.get(data)

# prepare the data as a pandas dataframe
df = pd.read_csv(data)
attributes = ["sepal_length", "sepal_width", "petal_length","petal_width", "class"]
df.columns = attributes

# create a pairplot to plot pairwise relationships in the dataset
plot = sns.pairplot(df, hue='class', palette='muted')
plot.savefig('pairwise_relationships.png')

# perform a K-nearest-neighbours classification with scikit-learn
# Step 1: split data in test and training dataset (20:80)
array = df.values
X = array[:,0:4]
Y = array[:,4]
test_size = 0.20
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y,
                                                                    test_size=test_size,
                                                                    random_state=seed)
# Step 2: Fit the model and make predictions on the test dataset
knn = KNeighborsClassifier()
knn.fit(X_train, Y_train)
predictions = knn.predict(X_test)

# Step 3: Save the classification report
report = classification_report(Y_test, predictions, output_dict=True)
df_report = pd.DataFrame(report).transpose().to_csv('prediction_report.csv')

EOT

Code snippet 130:

datalad status

Code snippet 131:

datalad save -m "add script for kNN classification and plotting" --version-tag ready4analysis code/script.py

Code snippet 132:

datalad run -m "analyze iris data with classification analysis" \
  --input "input/iris.csv" \
  --output "prediction_report.csv" \
  --output "pairwise_relationships.png" \
  "python3 code/script.py"

Code snippet 133:

git log --oneline

Code snippet 134:

# with the >| redirection we are replacing existing contents in the file
cat << EOT >| README.md

# Midterm YODA Data Analysis Project

## Dataset structure

- All inputs (i.e. building blocks from other sources) are located in input/.
- All custom code is located in code/.
- All results (i.e., generated files) are located in the root of the dataset:
  - "prediction_report.csv" contains the main classification metrics.
  - "output/pairwise_relationships.png" is a plot of the relations between features.

EOT

Code snippet 135:

datalad status

Code snippet 136:

datalad save -m "Provide project description" README.md

Code snippet 137:

# we are in the midterm_project subdataset
datalad containers-add midterm-software --url shub://adswa/resources:1

Code snippet 138:

git log -n 1 -p

Code snippet 139:

datalad containers-run -m "rerun analysis in container" \
  --container-name midterm-software \
  --input "input/iris.csv" \
  --output "prediction_report.csv" \
  --output "pairwise_relationships.png" \
  "python3 code/script.py"

Code snippet 140:

git log -p -n 1

Code snippet 141:

cd ../
datalad status

Code snippet 142:

datalad save -d . -m "add container and execute analysis within container" midterm_project