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###############################################
# Script settings and constants.
###############################################
SCRIPT_PATH = 'script.sql'

DB_CONNECTION = {
    'db_host': 'myhost.redshift.amazonaws.com', 
    'db_name': 'somedb', 
    'db_username': 'user',
    'db_password': 'pA$$word'

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var encrypt = function(plaintext, shiftAmount) {
    var ciphertext = "";
    for(var i = 0; i < plaintext.length; i++) {
        var plainCharacter = plaintext.charCodeAt(i);
        if(plainCharacter >= 97 && plainCharacter <= 122) {
            ciphertext += String.fromCharCode((plainCharacter - 97 + shiftAmount) % 26 + 97);
        } else if(plainCharacter >= 65 && plainCharacter <= 90) {
            ciphertext += String.fromCharCode((plainCharacter - 65 + shiftAmount) % 26 + 65);
        } else {
            ciphertext += String.fromCharCode(plainCharacter);

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from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local").appName("rel subject").getOrCreate()

columns = ['id', 'referringUrl']

vals = [
    (1, 'http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html'),
    (2, 'https://stackoverflow.com/questions/33224740/best-way-to-get-the-max-value-in-a-spark-dataframe-column'),
    (3, 'https://datascience.stackexchange.com/questions/11284/key-parameter-in-max-function-in-pyspark'),
]

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from pyspark.context import SparkContext

spark = SparkSession.builder.master("local").appName("rel subject").getOrCreate()

file_path = 'some_file.csv'
dataframe = spark.read.csv(path=file_path, header=True)
dataframe = dataframe.filter(dataframe['value'] >= 0.5)

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##################################
# The Philosopher: A program that
# generates philosophic quotes...
#################################
import random

R = ["without", "under",
      "between", "over", "on",
      "after", "by", "because of", "next to", "on top",
      "like", "through", "until", "from", "into", "to", "in",

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# Import necessary modules
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_val_score

# Compute predicted probabilities: y_pred_prob
y_pred_prob = logreg.predict_proba(X_test)[:,1]

# Compute and print AUC score
print("AUC: {}".format(roc_auc_score(y_test, y_pred_prob)))

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# Import necessary modules
from sklearn.metrics import roc_curve

# Compute predicted probabilities: y_pred_prob
y_pred_prob = logreg.predict_proba(X_test)[:,1]

# Generate ROC curve values: fpr, tpr, thresholds
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)

# Plot ROC curve

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# Import the necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Create the classifier: logreg
logreg = LogisticRegression()

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# Import the necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Create the classifier: logreg
logreg = LogisticRegression()

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var mongoose = require('mongoose');

var Schema = new mongoose.Schema({
    filename: String,
    path: String,
    creationDate: Date
});

var Image = mongoose.model('Image', Schema);