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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However, insofar as user input is concerned, we can test it _or_ we can use it as something called a "sentinel" value. Using word "sentinel" here means exactly what it means in normal speech -- something to watch out for, like the following: | # Setup choice
choice = ""
# Do the loop
while choice != "E":
# Print message to relay the user's choice
print("The loop is running.")
choice = input("[C]ontinue or [E]xit? ")
print("You chose to exit!") | _____no_output_____ | CC-BY-4.0 | day-2/0_week-0-worksheet-while-loops.ipynb | allegheny-college-cmpsc-100-jan-2021/week-0 |
Mean`while`The conclusion we should draw from our little detour is this: `while` statements are exceptionally good at handling all kinds of `boolean` conditions. When it's merely simple counting, like our countdown example above, it's OK, too -- but, as we'll see in the near future, counting and other more complex tes... | # NOTE: YOU MUST RUN THIS CELL TO MAKE THESE VARIABLES AVAILABLE
# NOTE 2: RUN THIS CELL TO RESET ALL NUMBERS AS WELL
# Setup variable to handle input
number = ""
# Setup variable to keep running total
sum = 0
# Setup a count variable to track the count
count = 0
# TODO: Write code to complete activity using knowle... | _____no_output_____ | CC-BY-4.0 | day-2/0_week-0-worksheet-while-loops.ipynb | allegheny-college-cmpsc-100-jan-2021/week-0 |
" backtesting with grid search"> "Easily backtest a grid of parameters in a given trading strategy"- toc: true- branch: master- badges: true- comments: true- author: Jerome de Leon- categories: [grid search, backtest] | # uncomment to install in colab
# !pip3 install fastquant | _____no_output_____ | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
backtest SMAC `fastquant` offers a convenient way to backtest several trading strategies. To backtest using Simple Moving Average Crossover (`SMAC`), we do the following.```pythonbacktest('smac', dcv_data, fast_period=15, slow_period=40)````fast_period` and `slow_period` are two `SMAC` parameters that can be changed d... | from fastquant import get_stock_data, backtest
symbol='JFC'
dcv_data = get_stock_data(symbol,
start_date='2018-01-01',
end_date='2020-04-28',
format='cv',
)
dcv_data.head()
import matplotlib.pyplot as pl
pl.style.use("default")
from fastq... | ===Global level arguments===
init_cash : 100000
buy_prop : 1
sell_prop : 1
===Strategy level arguments===
fast_period : 15
slow_period : 40
Final PnL: -31257.65
Time used (seconds): 0.10944700241088867
Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'execution_type': 'close', 'fast_period': 15,... | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
The plot above is optional. `backtest` returns a dataframe of parameters and corresponding metrics: | results.head() | _____no_output_____ | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
define the search space Second, we specify the range of reasonable values to explore for `fast_period` and `slow_period`. Let's take between 1 and 20 trading days (roughly a month) in steps of 1 day for `fast_period`, and between 21 and 240 trading days (roughly a year) in steps of 5 days for `slow_period`. | import numpy as np
fast_periods = np.arange(1,20,1, dtype=int)
slow_periods = np.arange(20,241,5, dtype=int)
# make a grid of 0's (placeholder)
period_grid = np.zeros(shape=(len(fast_periods),len(slow_periods)))
period_grid.shape | _____no_output_____ | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
run grid search Third, we run backtest for each iteration over each pair of `fast_period` and `slow_period`, saving each time the net profit to the `period_grid` variable. Note: Before running backtest over a large grid, try measuring how long it takes your machine to run one backtest instance.```python%timeitbacktest... | from time import time
init_cash=100000
start_time = time()
for i,fast_period in enumerate(fast_periods):
for j,slow_period in enumerate(slow_periods):
results = backtest('smac',
dcv_data,
fast_period=fast_period,
slow_peri... | Basic grid search took 100.4 sec
| MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
visualize the period grid Next, we visualize `period_grid` as a 2D matrix. | import matplotlib.colors as mcolors
import matplotlib.pyplot as pl
pl.style.use("default")
fig, ax = pl.subplots(1,1, figsize=(8,4))
xmin, xmax = slow_periods[0],slow_periods[-1]
ymin, ymax = fast_periods[0],fast_periods[-1]
#make a diverging color map such that profit<0 is red and blue otherwise
cmap = pl.get_cmap('... | max profit=7042 @ (105,3) days
| MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
From the plot above, there are only a few period combinations which we can guarantee non-negative net profit using SMAC strategy. The best result is achieved with (105,30) for period_slow and period_fast, respectively.In fact SMAC strategy is so bad such that there is only 9% chance it will yield profit when using any ... | percent_positive_profit=(period_grid>0).sum()/np.product(period_grid.shape)*100
percent_positive_profit
percent_breakeven=(period_grid==0).sum()/np.product(period_grid.shape)*100
percent_breakeven | _____no_output_____ | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
Anyway, let's check the results of backtest using the `best_fast_period` and `best_slow_period`. | results = backtest('smac',
dcv_data,
fast_period=best_fast_period,
slow_period=best_slow_period,
verbose=True,
plot=True
)
net_profit = results.final_value.values[0]-init_cash
net_profit | Starting Portfolio Value: 100000.00
===Global level arguments===
init_cash : 100000
buy_prop : 1
sell_prop : 1
===Strategy level arguments===
fast_period : 3
slow_period : 105
2018-08-22, BUY CREATE, 286.00
2018-08-22, Cash: 100000.0
2018-08-22, Price: 286.0
2018-08-22, Buy prop size: 346
2018-08-22, Afforded size: 346... | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
There are only 6 cross-over events of which only the latest transaction yielded positive gains resulting to a 7% net profit. Is 7% profit over a ~two-year baseline better than the market benchmark? built-in grid search in fastquant The good news is `backtest` provides a built-in grid search if strategy parameters are ... | from fastquant import backtest
start_time = time()
results = backtest("smac",
dcv_data,
fast_period=fast_periods,
slow_period=slow_periods,
verbose=False,
plot=False
)
end_time = time()
time_optimized ... | Optimized grid search took 95.6 sec
| MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
`results` is automatically ranked based on `rnorm` which is a proxy for performance. In this case, the best `fast_period`,`slow_period`=(8,200) d.The returned parameters are should have `len(fast_periods)`x`len(slow_periods)` (19x45=855 in this case). | results.shape
results.head() | _____no_output_____ | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
Now, we recreate the 2D matrix before, but this time using scatter plot. | fig, ax = pl.subplots(1,1, figsize=(8,4))
#make a diverging color map such that profit<0 is red and blue otherwise
cmap = pl.get_cmap('RdBu')
norm = mcolors.TwoSlopeNorm(vmin=period_grid.min(),
vmax = period_grid.max(),
vcenter=0
)... | max profit=7042 @ (105.0,3.0) days
| MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
Note also that built-in grid search in `backtest` is optimized and slightly faster than the basic loop-based grid search. | #time
time_basic/time_optimized | _____no_output_____ | MIT | examples/2020-04-20-backtest_with_grid_search.ipynb | johnmarkacala/fastquant |
IBM Data Science Specialization Course 9: Applied Data ScienceThis notebook will be mainly used for the capstone project. | import pandas as pd
import numpy as np
print ('Hello Capstone Project Course!')
# The code was removed by Watson Studio for sharing. | _____no_output_____ | CC-BY-4.0 | IBM_CRS9_W1_Capstone_Notebook.ipynb | mcmasty/Coursera_Capstone |
Python and MySQL First import the python module containing the API | import pymysql | _____no_output_____ | MIT | BAMM.101x/sql_and_python.ipynb | KECB/learn |
Set up a connection and create a cursor object | db = pymysql.connect("localhost","root","None" ,database="schooldb")
cursor = db.cursor()
| _____no_output_____ | MIT | BAMM.101x/sql_and_python.ipynb | KECB/learn |
Execute a query and get the results | cursor.execute('show tables;')
cursor.fetchall()
query = """
SELECT course.name FROM student
INNER JOIN enrolls_in ON student.ssn = enrolls_in.ssn
INNER JOIN course ON course.number = enrolls_in.class
WHERE f_name = "JOHN";
"""
cursor.execute(query)
cursor.fetchall()
# 向数据库插入数据
insert = 'INSERT INTO S... | _____no_output_____ | MIT | BAMM.101x/sql_and_python.ipynb | KECB/learn |
Preprocessing | # Import our dependencies
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
import tensorflow as tf
# Import and read the charity_data.csv.
import pandas as pd
application_df = pd.read_csv("Resources/charity_data.csv")
application_df.head()
# Dr... | (25724, 45)
| ADSL | Starter_Code.ipynb | ichakrabarty/Deep-Learning-Homework-Charity-Funding-Predictor |
Compile, Train and Evaluate the Model | # Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.
nn = tf.keras.models.Sequential()
# First hidden layer
nn.add(tf.keras.layers.Dense(units=80, activation ='relu', input_dim = 45))
# Second hidden layer
nn.add(tf.keras.layers.Dense(units=30, activation ='relu',... | _____no_output_____ | ADSL | Starter_Code.ipynb | ichakrabarty/Deep-Learning-Homework-Charity-Funding-Predictor |
Attempt 2 | # Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.
nn = tf.keras.models.Sequential()
# First hidden layer
nn.add(tf.keras.layers.Dense(units=80, activation ='relu', input_dim = 45))
# Second hidden layer
nn.add(tf.keras.layers.Dense(units=30, activation ='relu',... | _____no_output_____ | ADSL | Starter_Code.ipynb | ichakrabarty/Deep-Learning-Homework-Charity-Funding-Predictor |
Attempt 3 | # Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.
nn = tf.keras.models.Sequential()
# First hidden layer
nn.add(tf.keras.layers.Dense(units=80, activation ='relu', input_dim = 45))
# Second hidden layer
nn.add(tf.keras.layers.Dense(units=30, activation ='sigmoi... | _____no_output_____ | ADSL | Starter_Code.ipynb | ichakrabarty/Deep-Learning-Homework-Charity-Funding-Predictor |
Tabular Q-Learning From Scratch Custom Environment to train our model on | import gym
from gym import spaces
import numpy as np
import random
from copy import deepcopy
class gridworld_custom(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, *args, **kwargs):
super(gridworld_custom, self).__init__()
... | _____no_output_____ | MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Import Required Packages | import numpy as np
import matplotlib.pyplot as plt
from copy import deepcopy
import pandas as pd
#from tqdm.auto import tqdm
from tqdm import tqdm | _____no_output_____ | MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Build the q-learning class which contains the table storing all the q values for all states and actions | class q_learning():
def __init__(self):
self.q_table = pd.DataFrame(columns=['state', 'q_val_0', 'q_val_1', 'q_val_2', 'q_val_3'])
for i in range(5):
for j in range(5):
state_str = "("+str(i)+","+str(j)+")"
X = pd.DataFrame([[state_str, 0, 0, 0, 0]],... | _____no_output_____ | MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Check up the functionality of epsilon greedy. Just for reference. | epsilon = 1
epsilon_decay = 0.9997
episodes = 10000
epsilon_copy = deepcopy(epsilon)
eps = []
for i in range(episodes):
epsilon_copy = epsilon_copy * epsilon_decay
eps.append(epsilon_copy)
plt.plot(eps)
plt.show() | _____no_output_____ | MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Run everything. | env = gridworld_custom()
agent = q_learning()
pbar = tqdm(range(episodes))
for episode in pbar:
prev_obs = env.reset()
done = False
epsilon = epsilon * epsilon_decay
while not(done):
if(random.uniform(0, 1) > epsilon):
action = agent.choose_action(prev_obs)
else:
... | 100%|███████████████████████████████████████████████████████████████████████████| 10000/10000 [00:23<00:00, 434.64it/s]
| MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Take a look at the Q Table after training. Gives us an understanding as to how the model might function | agent.q_table | _____no_output_____ | MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Test the trained model | prev_obs = env.reset()
done = False
env.render()
while not(done):
action = agent.choose_action(prev_obs)
obs, reward, done, _ = env.step(action)
prev_obs = obs
env.render() | _ _ _ _ _
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* _ D _ w
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_ ... | MIT | Week1/prerun_code/3_Q_learning_scratch_prerun.ipynb | ashwin-M-D/Northeastern_RL_Discussions |
Abstractive Summarization Loading Pre-processed DatasetThe Data is preprocessed in [Data_Pre-Processing.ipynb](https://github.com/JRC1995/Abstractive-Summarization/blob/master/Data_Pre-Processing.ipynb)Dataset source: https://www.kaggle.com/snap/amazon-fine-food-reviews | import json
with open('Processed_Data/Amazon_Reviews_Processed.json') as file:
for json_data in file:
saved_data = json.loads(json_data)
vocab2idx = saved_data["vocab"]
embd = saved_data["embd"]
train_batches_text = saved_data["train_batches_text"]
test_batches_text = save... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Hyperparameters | hidden_size = 300
learning_rate = 0.001
epochs = 5
max_summary_len = 16 # should be summary_max_len as used in data_preprocessing with +1 (+1 for <EOS>)
D = 5 # D determines local attention window size
window_len = 2*D+1
l2=1e-6 | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Tensorflow Placeholders | import tensorflow as tf
embd_dim = len(embd[0])
tf_text = tf.placeholder(tf.int32, [None, None])
tf_embd = tf.placeholder(tf.float32, [len(vocab2idx),embd_dim])
tf_true_summary_len = tf.placeholder(tf.int32, [None])
tf_summary = tf.placeholder(tf.int32,[None, None])
tf_train = tf.placeholder(tf.bool) | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Embed vectorized textDropout used for regularization (https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) | embd_text = tf.nn.embedding_lookup(tf_embd, tf_text)
embd_text = tf.layers.dropout(embd_text,rate=0.3,training=tf_train) | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
LSTM functionMore info: https://dl.acm.org/citation.cfm?id=1246450, https://www.bioinf.jku.at/publications/older/2604.pdf,https://en.wikipedia.org/wiki/Long_short-term_memory | def LSTM(x,hidden_state,cell,input_dim,hidden_size,scope):
with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
w = tf.get_variable("w", shape=[4,input_dim,hidden_size],
dtype=tf.float32,
trainable=True,
... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Bi-Directional LSTM Encoder(https://maxwell.ict.griffith.edu.au/spl/publications/papers/ieeesp97_schuster.pdf)More Info: https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/Bi-directional LSTM encoder has a forward encoder and a backward encoder. The forward encoder encod... | S = tf.shape(embd_text)[1] #text sequence length
N = tf.shape(embd_text)[0] #batch_size
i=0
hidden=tf.zeros([N, hidden_size], dtype=tf.float32)
cell=tf.zeros([N, hidden_size], dtype=tf.float32)
hidden_forward=tf.TensorArray(size=S, dtype=tf.float32)
#shape of embd_text: [N,S,embd_dim]
embd_text_t = tf.transpose(embd_... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Backward Encoding | i=S-1
hidden=tf.zeros([N, hidden_size], dtype=tf.float32)
cell=tf.zeros([N, hidden_size], dtype=tf.float32)
hidden_backward=tf.TensorArray(size=S, dtype=tf.float32)
def cond(i, hidden, cell, hidden_backward):
return i >= 0
def body(i, hidden, cell, hidden_backward):
x = embd_text_t[i]
hidden,cell = LSTM(x... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Merge Forward and Backward Encoder Hidden States | hidden_forward = hidden_forward.stack()
hidden_backward = hidden_backward.stack()
encoder_states = tf.concat([hidden_forward,hidden_backward],axis=-1)
encoder_states = tf.transpose(encoder_states,[1,0,2])
encoder_states = tf.layers.dropout(encoder_states,rate=0.3,training=tf_train)
final_encoded_state = tf.layers.dr... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Implementation of attention scoring functionGiven a sequence of encoder states ($H_s$) and the decoder hidden state ($H_t$) of current timestep $t$, the equation for computing attention score is:$$Score = (H_s.W_a).H_t^T $$($W_a$ = trainable parameters)(https://nlp.stanford.edu/pubs/emnlp15_attn.pdf) | def attention_score(encoder_states,decoder_hidden_state,scope="attention_score"):
with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
Wa = tf.get_variable("Wa", shape=[2*hidden_size,2*hidden_size],
dtype=tf.float32,
trainable=True,
... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Local Attention FunctionBased on: https://nlp.stanford.edu/pubs/emnlp15_attn.pdf |
def align(encoder_states, decoder_hidden_state,scope="attention"):
with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
Wp = tf.get_variable("Wp", shape=[2*hidden_size,125],
dtype=tf.float32,
trainable=True,
... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
LSTM Decoder With Local Attention | with tf.variable_scope("decoder",reuse=tf.AUTO_REUSE):
SOS = tf.get_variable("sos", shape=[1,embd_dim],
dtype=tf.float32,
trainable=True,
initializer=tf.glorot_uniform_initializer())
# SOS represents starting ma... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Define Cross Entropy Cost Function and L2 Regularization | filtered_trainables = [var for var in tf.trainable_variables() if
not("Bias" in var.name or "bias" in var.name
or "noreg" in var.name)]
regularization = tf.reduce_sum([tf.nn.l2_loss(var) for var
in filtered_trainables])
with tf.variable... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Accuracy | # Comparing predicted sequence with labels
comparison = tf.cast(tf.equal(outputs, tf_summary),
tf.float32)
# Masking to ignore the effect of pads while calculating accuracy
pad_mask = tf.sequence_mask(tf_true_summary_len,
maxlen=max_summary_len,
... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Define Optimizer | all_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
optimizer = tf.contrib.opt.NadamOptimizer(
learning_rate=learning_rate)
gvs = optimizer.compute_gradients(cost, var_list=all_vars)
capped_gvs = [(tf.clip_by_norm(grad, 5), var) for grad, var in gvs] # Gradient Clipping
train_op = optimizer.apply_gra... | _____no_output_____ | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Training and Validation | import pickle
import random
with tf.Session() as sess: # Start Tensorflow Session
display_step = 100
patience = 5
load = input("\nLoad checkpoint? y/n: ")
print("")
saver = tf.train.Saver()
if load.lower() == 'y':
print('Loading pre-trained weights for the model...')
saver.... |
Load checkpoint? y/n: n
STARTING TRAINING
Iter 0, Cost= 2.086, Acc = 0.00%
Sample Text
i 'm not a big pretzel eater , but i love this little <UNK> nibblers . i like the low fat snack and how it fills you up .
Sample Predicted Summary
municipality jackass municipality mongolian seats han han mongolian hah sus ... | MIT | .ipynb_checkpoints/Summarization-checkpoint.ipynb | ParasAvkirkar/AbstractiveTextSummarization |
Чтение и подготовка обучающей выборки | train = pd.read_csv("./example_data/train_demo.csv",
low_memory=False,
index_col="line_id",
parse_dates = ["datetime_" + str(i) for i in range(2)],)
train = train.iloc[:, 50:100]
num_col = list(filter(lambda x: "numb" in x, train.columns))
num_feature_type =... | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
Чтение и подготовка тестовой выборки | test = pd.read_csv("./example_data/test_demo.csv",
index_col="line_id",
parse_dates = ["datetime_" + str(i) for i in range(2)])
date_col = filter(lambda x: "datetime" in x, test.columns)
for col in date_col:
test[col + "_year"] = test[col].map(lambda x: x.year)
test[col +... | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
Параметры модели Для обучения модели рекомендуется указать тип признаков для обучения.Поэтому создается словарь features_type с ключами: "real" -- вещественный признак,"cat" -- категориальный.Для признаков, которые не размечены, типы будут определены автоматом. Такой вариант будет работать, но качество порядочно прос... | cat_col = list(filter(lambda x: "str" in x, train.columns))
cat_feature_type = {x: "cat" for x in cat_col}
year_col = list(filter(lambda x: "_year" in x, train.columns))
year_feature_type = {x: "cat" for x in year_col}
weekday_col = list(filter(lambda x: "_weekday" in x, train.columns))
weekday_feature_type = {x: "ca... | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
Feature level constrains | features_type = dict(**num_feature_type,
**cat_feature_type,
**year_feature_type,
**weekday_feature_type,
**month_feature_type) | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
- `features_monotone_constraints` - также можно указать зависимость целевой переменной от признака. Если заранее известно, что при возрастании признака feature_1, то эту информацию можно учесть в модели, добавив в словарь пару {feature_1: "1"}. Если же зависимость признака от целевой переменной обратная, то можно указа... | features_monotone_constraints = {'number_74': 'auto', 'number_83': 'auto'} | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
- `max_bin_count` - через словарь max_bin_count можно задать число бинов для WoE кодирования, если для какого-то признака оно отлично от общего. | max_bin_count = {'number_47': 3, 'number_51': 2} | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
РекомендацияВ общем случае, в первый момент построения модели лучше не указывать специальных ограничений в features_monotone_constraints и max_bin_count. Если в результате анализа полученной модели разбиение оказалось неинтерпретируемым или нестабильным по отдельным признакам, но в целом по модели ок, то ограничить с... | auto_woe = AutoWoE(interpreted_model=True,
monotonic=False,
max_bin_count=5,
select_type=None,
pearson_th=0.9,
auc_th=.505,
vif_th=10.,
imp_th=0,
th_con... | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
- `train` обучающая выборка- `target_name` - название целевой переменной- `features_type` - см выше описание дикта features_type. Возможно указание None для автозаполнения, но не рекомендуется- `group_kf` - название колонки-группы для GroupKFold https://scikit-learn.org/stable/modules/generated/sklearn.model_selection... | auto_woe.fit(train[features + ['target']],
target_name="target",
features_type=features_type,
group_kf=None,
max_bin_count=max_bin_count,
features_monotone_constraints=features_monotone_constraints,
validation=test
)
pred = auto_... | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
Полезные методы модели - `private_features_type` - типизация признаков- `get_woe` - рабиение на бины и WoE значения в них- `get_split` - границы разбиения. Особо полезен для категориальных признаков Замечание: ReportDeco - обертка для построения отчета. Она не обязательна для обучения и применения модели, но обязатель... | report_params = {"automl_date_column": "report_month", # колонка с датой в формате params['datetimeFormat']
"output_path": "./AUTOWOE_REPORT_1", # папка, куда сгенерится отчет и сложатся нужные файлы
"report_name": "___НАЗВАНИЕ ОТЧЕТА___",
"report_version_id": 1,
... | _____no_output_____ | Apache-2.0 | (Example_1) BasicUsageAndParams.ipynb | VitaliyPavlyukov/AutoMLWhitebox |
Discussion Analysis===Notebook for analysis of discussion done in Evidence and Reconsider tasks via the annotation web client. | import os
import re
import pandas as pd
import numpy as np
import sklearn
import sklearn.metrics
from collections import Counter
import itertools
import sqlite3
import sys
sys.path.append("../annotation_data")
import responsibility as responsibility_utils
from utils import get_webclient_url
annotation_web_client_databa... | _____no_output_____ | MIT | patient_responsibilities/discussion-analysis.ipynb | levon003/icwsm-cancer-journeys |
Experiment metadata | responsibility_list = ["coordinating_support",
"symptom_management",
"preparation",
"managing_transitions",
"info_filtering",
"continued_monitoring",
"clinical_decisions"]
user1 = "luoxx498"
use... | _____no_output_____ | MIT | patient_responsibilities/discussion-analysis.ipynb | levon003/icwsm-cancer-journeys |
Load data | all_rows = []
for phase in [evidence_phase, reconsider_phase]:
for users in [(user1, user2), (user2, user1)]:
evidence_username, reconsider_username = users
for responsibility in responsibility_list:
new_rows = get_discussion_entries(responsibility, phase, evidence_username, reconsider_u... | _____no_output_____ | MIT | patient_responsibilities/discussion-analysis.ipynb | levon003/icwsm-cancer-journeys |
Analysis | # first, how many fall into the three conditions?
print("Evidence phase annotation changes")
for u1, u2 in [(user1, user2), (user2, user1)]:
print("Evidence tasks:", u1)
print("Reconsider tasks:", u2)
print("="*40)
for responsibility in responsibility_list:
df_subset = df[(df.phase == evidence_p... | Reconsider phase annotation changes
Evidence tasks: luoxx498
Reconsider tasks: eriks074
========================================
coordinating_support 12/14 85.7%
symptom_management 15/16 93.8%
preparation 15/17 88.2%
managing_transitions 0/ 0 ... | MIT | patient_responsibilities/discussion-analysis.ipynb | levon003/icwsm-cancer-journeys |
Irresolvable case analysis | print("Irresolvable disagreements from reconsider phase")
print("="*50)
u1_all_irresolvable = 0
u2_all_irresolvable = 0
u1_all_total = 0
u2_all_total = 0
for responsibility in responsibility_list:
df_subset = df[(df.phase == reconsider_phase)
& (df.responsibility == responsibility)
... | _____no_output_____ | MIT | patient_responsibilities/discussion-analysis.ipynb | levon003/icwsm-cancer-journeys |
Themes in comments: - Evidence to me of a different responsibility (and thus not this one) Takeaway: A problem with soft boundaries? Evidence that lies in margins especially hard to interpret - Not clear enough (in other words, ambiguous) - An edge case that falls just outside the boundary Qualitative analysis ... | pd.set_option('display.max_colwidth', 255)
df[(df.additional_discussion != "") & (df.phase == evidence_phase)][["responsibility", "highlighted_text", "additional_discussion", "evidence_username", "reconsider_username", "is_annotation_changed"]]
df[(df.additional_discussion != "") & (df.phase == reconsider_phase)][["res... | _____no_output_____ | MIT | patient_responsibilities/discussion-analysis.ipynb | levon003/icwsm-cancer-journeys |
Note how the not operator is ~ and non **not** | df = pd.DataFrame(output)
yy = '2011'
dfc = df[ df['created'].str.contains(yy) ]
dfu = df[ ~df['created'].str.contains(yy) ]
math_cat = [ x for x in set( ' '.join(list(df['categories'])).split(' ') ) if 'math' in x]
len(dfc)
def date2doy(x, yy=2010):
if x == '' : return None
yy,mm,dd = [int(y) for y in x.split(... | _____no_output_____ | MIT | Arxiv_stats.ipynb | macbuse/Arxiv-Api |
Learn you bash | bash_lessons = !find ~ | grep -1 LearnYou/learnyoubash
bash_lessons[2] # = bash_lessons[1]
!cat '/Users/frank/Documents/JS/LearnYou/learnyoubash/arr.bash' | _____no_output_____ | MIT | _productivity/learnyoubash.ipynb | aixpact/data-science |
Arrays | %%!
fruits=(Apple Pear Plum)
echo ${fruits[*]:0:2} | _____no_output_____ | MIT | _productivity/learnyoubash.ipynb | aixpact/data-science |
Valores nulos em data frame | import pandas as pd
import numpy as np
# Importando o dataset
df = pd.read_csv("data_sets/pima-data.csv")
df.shape
df.head()
# Verificando se existem valores nulos
df.isnull().values.any() | _____no_output_____ | MIT | tricks/valores_nulos.ipynb | everton3x/python-tricks |
_Lambda School Data Science, Unit 2_ Regression 2 Sprint Challenge: Predict drugstore sales 🏥For your Sprint Challenge, you'll use real-world sales data from a German drugstore chain, from Jan 2, 2013 — July 31, 2015.You are given three dataframes:- `train`: historical sales data for 100 stores- `test`: historical sa... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import category_encoders as ce
import eli5
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_squared_log_error
from sklearn.model_selection import cross_val_score
from sk... | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
The dataframes have a variety of columns:- **Store** - a unique Id for each store- **DayOfWeek** - integer, 1-6- **Date** - the date, from Jan 2, 2013 — July 31, 2015.- **Sales** - the units of inventory sold on a given date (this is the target you are predicting)- **Customers** - the number of customers on a given dat... | store.head()
train.head()
test.head() | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Merge train, test and store | # Wrangle train, validation, and test sets
def wrangle(X):
X = X.copy()
# Engineer date features
X['Date'] = pd.to_datetime(X['Date'], infer_datetime_format=True)
X['date_year'] = X['Date'].dt.year
X['date_month'] = X['Date'].dt.month
X['date_day'] = X['Date'].dt.day
X = X.drop(columns=... | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Arrange X matrix and y target for train and test | target = 'Sales'
X_train = train.drop(columns=target)
X_test = test.drop(columns=target)
y_train = train[target]
y_test = test[target]
X_train.shape, X_test.shape, y_train.shape, y_test.shape | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
log transform train and test target | y_train_log = np.log1p(y_train)
y_test_log = np.log1p(y_test) | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
plot distribution of train target before and after transformation | sns.distplot(y_train);
sns.distplot(y_train_log); | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
2. Fit and validate your model- **Use Gradient Boosting** or any type of regression model.- **Beat the baseline:** The estimated baseline Root Mean Squared Logarithmic Error is 0.90, if we guessed the mean sales for every prediction. Remember that RMSE with the log-transformed target is equivalent to RMSLE with the or... | def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
def rmsle(y_true, y_pred):
return np.sqrt(mean_squared_log_error(y_true, y_pred)) | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Build baseline model | y_base = np.full_like(y_test_log, fill_value=y_train_log.mean())
print('Validation RMSLE, Mean Baseline:', rmse(y_test_log, y_base)) | Validation RMSLE, Mean Baseline: 0.3948331905413732
| MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Split train data into train and validate, put all dates for a given store in the same set | stores = train['Store'].unique()
train_stores, val_stores = train_test_split(
stores, random_state=42
)
train_stores = train[train.Store.isin(train_stores)]
val_stores = train[train.Store.isin(val_stores)]
assert train_stores.shape[0] + val_stores.shape[0] == train.shape[0]
target = 'Sales'
X_train = train_stor... | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Use XGBoost to predict target | # Make pipeline
pipeline = make_pipeline(
ce.OrdinalEncoder(),
XGBRegressor(
n_estimators=1000,
n_jobs=-1,
eval_metric='rmse',
early_stopping_rounds=10
)
)
# Fit
pipeline.fit(X_train, y_train_log)
# Validate
y_pred_log = pipeline.predict(X_val)
print('Validation Error', r... | [20:08:19] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
Validation Error 0.1641765041445393
| MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Get the test error | y_pred_test = pipeline.predict(X_test)
print('Test Error', rmse(y_test_log, y_pred_test)) | Test Error 0.15633498742026974
| MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
3. Plot model interpretation visualizations- Choose any one of these options: - Permutation Importances plot - Partial Dependency Plot, 1 feature isolation - Partial Dependency Plot, 2 feature interaction Stretch goals- Plot 2+ visualizations.- Use permutation importances for feature selection. | X_val.columns
features = [
'date_month',
'date_day',
'DayOfWeek'
]
for feature in features:
isolated = pdp_isolate(
model=pipeline,
dataset=X_val,
model_features=X_val.columns,
feature=feature
)
pdp_plot(isolated, feature_name=feature); | _____no_output_____ | MIT | DS_Sprint_Challenge_8_Regression_2.ipynb | AndrewMarksArt/DS-Unit-2-Applied-Modeling |
Deutsch Algorithmby Olga Okrut Install frameworks, and import libraries | !pip install tensornetwork jax jaxlib colorama qcircuit
from qcircuit import QCircuit as qc | _____no_output_____ | Apache-2.0 | tutorials/Deutsch_Algorithm.ipynb | MadhavJivrajani/QCircuit |
Now, after we have learned how quantum gates work and how to build a quantum circuit, we will jump to the first quantum algorithm. We begin with a very simple quantum algorithm - Deutsch algorithm, named after its inventor David Deutsch, which serves as an excellent proof of the supremacy of quantum computers and algor... | # define binary functions. Some of them are constant, other balanced
def f1(x):
return x
def f2(x):
return 1
def f3(x):
return 0
def f4(x):
return not x
def f5(x):
return x ** 2
def f6(x):
return not (x ** 3)
def f7(x):
return (x % 3 == 2)
def f8(x):
return not (x % 3 == 2)
# check i... | /usr/local/lib/python3.6/dist-packages/jax/lib/xla_bridge.py:123: UserWarning: No GPU/TPU found, falling back to CPU.
warnings.warn('No GPU/TPU found, falling back to CPU.')
| Apache-2.0 | tutorials/Deutsch_Algorithm.ipynb | MadhavJivrajani/QCircuit |
Read and display data from a CGNS file | import h5py
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | MIT | read-cgns.ipynb | mdpiper/notebooks |
Access a CGNS file. | f = h5py.File('data/Test1.cgn', 'r')
list(f.keys()) | _____no_output_____ | MIT | read-cgns.ipynb | mdpiper/notebooks |
Move through the tree to find the x and y coordinate grids. | grid = f['iRIC']['iRICZone']['GridCoordinates']
x = grid['CoordinateX'][' data'] # yes, there's a space
y = grid['CoordinateY'][' data']
x.shape, y.shape
x.value | _____no_output_____ | MIT | read-cgns.ipynb | mdpiper/notebooks |
Display the coordinate grid. | plt.scatter(x, y) | _____no_output_____ | MIT | read-cgns.ipynb | mdpiper/notebooks |
Zoom in to see individula nodes. | plt.scatter(x, y)
plt.ylim([5,15])
plt.xlim([0,10]) | _____no_output_____ | MIT | read-cgns.ipynb | mdpiper/notebooks |
Table of Contents1 Stop Reinventing Pandas2 First Hacks!2.1 Beautiful pipes!2.2 The Penny Drops2.3 Map with dict3 Time Series3.1 Resample3.1.1 The Old Way3.1.2 A Better Way3.2 Slice Easily3.3 Time Wi... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use(['classic', 'ggplot', 'seaborn-poster', 'dean.style'])
%load_ext autoreload
%autoreload 2
import my_utils
import warnings
warnings.simplefilter("ignore") | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
First Hacks! Reading the data and a few housekeeping tasks. is the first place we can make our code more readable. | df_io = pd.read_csv('./bear_data.csv', index_col=0, parse_dates=['date_'])
df_io.head()
df = df_io.copy().sort_values('date_').set_index('date_').drop(columns='val_updated')
df.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Beautiful pipes!One line method chaining is hard to read and prone to human error, chaining each method in its own line makes it a lot more readable. | df_io\
.copy()\
.sort_values('date_')\
.set_index('date_')\
.drop(columns='val_updated')\
.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
But it has a problem. You can't comment out and even comment in between | # This block will result in an error
df_io\
.copy()\ # This is an inline comment
# This is a regular comment
.sort_values('date_')\
# .set_index('date_')\
.drop(columns='val_updated')\
.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Even an unnoticeable space character may break everything | # This block will result in an error
df_io\
.copy()\
.sort_values('date_')\
.set_index('date_')\
.drop(columns='val_updated')\
.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
The Penny DropsI like those "penny dropping" moments, when you realize you knew everything that is presented, yet it is presented in a new way you never thought of. | # We can split these value inside ()
users = (134856, 195373, 295817, 294003, 262166, 121066, 129678, 307120, 258759, 277922, 220794, 192312,
318486, 314631, 306448, 297059,206892,
169046, 181703, 146200, 199876, 247904, 250884, 282989, 234280, 202520,
138064, 133577, 301053, 24... | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Map with dictA dict is a callable with $f(key) = value$, there for you can call `.map` with it. In this example I want to make int key codes into letter. | df.bear_type.map(lambda x: x+3).head()
# A dict is also a callable
bears = {
1: 'Grizzly',
2: 'Sun',
3: 'Pizzly',
4: 'Sloth',
5: 'Polar',
6: 'Cave',
7: 'Black',
8: 'Panda'
}
df['bear_type'] = df.bear_type.map(bears)
df.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Time Series ResampleTask: How many events happen each hour? The Old Way | bad = df.copy()
bad['day'] = bad.index.date
bad['hour'] = bad.index.hour
(bad
.groupby(['day','hour'])
.count()
) | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
* Many lines of code* unneeded columns* Index is not a time anymore* **missing rows** (Did you notice?) A Better Way | df.resample('H').count() # H is for Hour | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
But it's even better on non-round intervals | rs = df.resample('10T').count()
# T is for Minute, and pandas understands 10 T, it will also under stand 11T if you wonder
rs.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
[Complete list of Pandas' time abbrevations](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Period.strftime.html) Slice EasilyPandas will automatically make string into timestamps, and it will understand what you want it to do. | # Take only timestamp in the hour of 21:00.
rs.loc['2018-10-09 21',:]
# Take all time stamps before 18:31
rs.loc[:'2018-10-09 18:31',:] | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Time Windows: Rolling, Expanding, EWMIf your Dataframe is indexed on a time index (Which we have) | fig, ax = plt.subplots()
rs.rename(columns = {'bear_type':'bears'}).plot(ax=ax,linestyle='--')
(rs
.rolling('90T')
.mean()
.rename(columns = {'bear_type':'rolling mean'})
.plot(ax=ax)
)
rs.expanding().mean().rename(columns = {'bear_type':'expanding mean'}).plot(ax=ax)
rs.ewm(6).mean().rename(columns = {'bear_type'... | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
With ApplyIntuitively, windows are like GroupBy, so you can apply anything you want after the grouping, e.g.: geometric mean. | fig, ax = plt.subplots()
rs.plot(ax=ax,linestyle='--')
(rs
.rolling(6)
.apply(lambda x: np.power(np.product(x),1/len(x)),raw=True)
.rename(columns = {'bear_type':'Rolling Geometric Mean'})
.plot(ax=ax)
)
plt.show() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Combine with GroupBy 🤯Pandas has no problem with groupby and resample together. It's as simple as `groupby[col1,col2]`. In our specific case, we want to cound events in an interval per event type. | per_bear = (df
.groupby('bear_type')
.resample('15T')
.apply('count')
.rename(columns={'bear_type':'amount'})
)
per_bear.groupby('bear_type').head(2) | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Sorting By Values | per_bear.sort_values(by=['amount'], ascending=False).head(10) | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
By Index | per_bear.sort_index().head(7)
per_bear.sort_index(level=1).head(7) | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
By Both (New in 0.23)`Index` has a name. Modern Pandas knows to address this index by name just like a regular column. | per_bear.sort_values(['amount','bear_type'], ascending=(False, True)).head(10) | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
Stack, Unstack Unstack In this case, working with a wide format indexed on intervals, with event types as columns, will make a lot more sense. The Old wayPivot table in modern pandas is more robust than it used to be. Still, it requires you to specify everything. | pt = pd.pivot_table(per_bear,values = 'amount',columns='bear_type',index='date_')
pt.head() | _____no_output_____ | MIT | _Nightly.ipynb | DeanLa/dont_reinvent_pandas |
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