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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The score will always be an integer since it is based on upvotes and downvotes. Before converting however, we need to check if there are any null values. | df.isna().sum()
df[df.isnull().any(axis=1)].head(20) | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
There is only a small amount of null values and they appear to be of little use, so removing them seems to be the best bet. Once the null values are removed we can convert score to an integer. | df = df.dropna()
df['score'] = df['score'].astype('int')
print(df.shape)
df.head(10) | (1941086, 3)
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Initial Data AnalysisBefore getting into handling the comment body a better understanding of the score collumn needs to be gained. | df['score'].describe()
sns.distplot(df["score"], kde=False) | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
As seen standard deviation and the distribution plot, there is a large distribution of data which makes the dataset skewed. In order to solve this log sclaling can be applied which might be useful later on. | mask = df["score"] > 0
sns.distplot(np.log1p(df["score"][mask]), kde=False) | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
The positive scores appear to be skewed with a significant majority of values being equal to 1. | mask = df["score"] < 0
sns.distplot(-np.log1p(-df["score"][mask]), kde=False) | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
The negative scores also seem a little skewed. Adding another score columnIn order to understand the data better and also create a logistic regression model a seperate column was created with the values of positive, negative or one score. Positive score being anything greater than 1, negative being anything less than ... | df['pn_score'] = ""
for i in df['score'].index:
if df['score'].at[i] > 1:
df['pn_score'].at[i] = 'positive'
elif float(df['score'].at[i]) <= 0:
df['pn_score'].at[i] = 'negative'
else:
df['pn_score'].at[i] = 'one'
df.head(10)
pn_counts = df['pn_score'].value_counts()
print(pn_cou... | positive 1053684
one 739088
negative 148314
Name: pn_score, dtype: int64
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Again there is an issue with distribution here. The majority of dataset has positive score values, where negative scores are much less frequent. Logistic Regression ModelThere will be a combination of logistic regression and linear regression models used.The logistic model will be created based on the categorical scor... | log_vect = TfidfVectorizer(max_df = 0.95, min_df = 5, binary=True, stop_words='english')
text_features = log_vect.fit_transform(df.body)
print(text_features.shape)
list(log_vect.vocabulary_)[:10]
encoder = LabelEncoder()
numerical_labels = encoder.fit_transform(df['pn_score'])
training_X, testing_X, training_y, testin... | [2 2 2 ... 0 0 1]
Accuracy: 0.5961028042005309
Classes: ['negative' 'one' 'positive']
Confusion Matrix:
[[ 0 5712 31367]
[ 0 37538 147234]
[ 0 11687 251734]]
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Since the data is so skewed a simple random over-sampling was used in order to increase the number of negative scores. The reason for using over-sampling as opposed to under-sampling is because we didn't want to loose any comments that could contribute as predictors. This does run the risk of overfitting the data howev... | count_pos, count_one, count_neg = df['pn_score'].value_counts()
df_pos_score = df[df['pn_score'] == 'positive']
df_neg_score = df[df['pn_score'] == 'negative']
df_one_score = df[df['pn_score'] == 'one']
df_neg_score_over = df_neg_score.sample(count_one, replace=True)
df_score_over = pd.concat([df_pos_score, df_neg_sc... | Random over-sampling:
positive 1053684
one 739088
negative 739088
Name: pn_score, dtype: int64
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Similarily to first model the comments need to be vectorized. | log_vect_over = TfidfVectorizer(max_df = 0.95, min_df = 5, binary=True, stop_words='english')
text_features = log_vect_over.fit_transform(df_score_over.body)
print(text_features.shape)
list(log_vect_over.vocabulary_)[:10] | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Now that the comments are turned into vectorized features they can be used in the logistic regression model. In order to achieve better results the random over-sampled data is used. | encoder = LabelEncoder()
numerical_labels = encoder.fit_transform(df_score_over['pn_score'])
training_X, testing_X, training_y, testing_y = train_test_split(text_features,
numerical_labels,
str... | [2 1 1 ... 0 2 0]
Accuracy: 0.4774545196021897
Classes: ['negative' 'one' 'positive']
Confusion Matrix:
[[ 33626 10591 140555]
[ 16730 25612 142430]
[ 13682 6765 242974]]
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
According to the confusion matrix the model struggles with determining a comment that has a score of 1 and usually mistakes it for a positive comment. It seems to perform the best with negative comments which could indicate overfitting of the data. Linear Regression ModelsThere will be two linear regression models, on... | pos_score_df = df[df.pn_score == 'positive']
pos_score_df.head() | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Similarily to the logistic regression the comments need to be transformed into a vector of numerical values. | pos_vect = TfidfVectorizer(max_df = 0.95, min_df = 5, binary=True, stop_words='english')
text_features = pos_vect.fit_transform(pos_score_df.body)
print(text_features.shape)
list(pos_vect.vocabulary_)[:10] | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Now that the comments are vectorized, the model can be created. In order to eliminate the issue with large distribution noticed during the alaysis, the scores are log scaled. | X_train, X_test, y_train, y_test = train_test_split(text_features, np.log1p(pos_score_df['score']))
pos_linear_regression = SGDRegressor(max_iter=1500)
pos_linear_regression.fit(X_train, y_train)
test = pos_linear_regression.predict(X_test)
mse = mean_squared_error(y_test, test)
rmse = np.sqrt(mse)
print()
print("Posi... |
Positive Score Model MSE: 0.8749582956086429
Positive Score Model RMSE: 0.935392054493004
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Based on the rmse the model seems to preform pretty well. Negative ScoresThe second linear regression model will predict the negative scores. Similarily to the first model only the rows with negative scores are necessary and the comment need to be vectorized using those. | neg_score_df = df[df.pn_score == 'negative']
neg_score_df.head()
neg_vect = TfidfVectorizer(max_df = 0.95, min_df = 5, binary=True, stop_words='english')
text_features = neg_vect.fit_transform(neg_score_df.body)
print(text_features.shape)
list(neg_vect.vocabulary_)[:10]
X_train, X_test, y_train, y_test = train_test_sp... |
Negative Score Model MSE: 1.0130192799066775
Negative Score Model RMSE: 1.0064885890593482
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
The results are similar to the first model. Combining ModelsFirst the logistic regression model will be used to preditc whether or not the score is negative or positive, then depending on the outcome the appropriate linear regression model will be used to predict the score value | a = (["You sir a simple idiot. Or a Russian bot. Either way not worth an actual sentence on why I didn't vote for that loon."])
logistic_result = logistic_regression_over.predict(log_vect_over.transform(a))
print('Logistic Result: ')
print(logistic_result)
print()
if(logistic_result) == 2:
linear_result = pos_lin... | Logistic Result:
[0]
Linear Result:
[-1.09910057]
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Lastly, we want to pickle our models and vectorizers for deployment. | import pickle
pickle.dump(logistic_regression_over, open('logreg.pkl', 'wb'))
pickle.dump(pos_linear_regression, open('poslinreg.pkl', 'wb'))
pickle.dump(neg_linear_regression, open('neglinreg.pkl', 'wb'))
pickle.dump(log_vect_over, open('log_vect.pkl', 'wb'))
pickle.dump(pos_vect, open('pos_vect.pkl', 'wb'))
pickle.du... | _____no_output_____ | MIT | python/redditscore.ipynb | AlexHartford/redditscore |
`Note:` All assignment should be done inside the notebook (Double tap on each of this text to edit). `Question 1`: IRENE UMOH 17100310866 `Question 2`: What do you understand by natural language processing NLP is a subfield of artificial Intelligence (AI). In simple terms, Natural Language Processing is the ability... | a = ' In the end, he realized he could see sound and hear words. '
b = 'I ate a sock because people on the Internet told me to'
c = 'She had a car and she also had a car'
d = 'The skeleton had skeletons of his own in the closet'
e = 'peered'
a1=a.split(' ')
b1=b.split(' ')
c1=c.split(' ')
d1=d.split(' ')
e1=e.split('e'... | _____no_output_____ | MIT | IRENE UMOH Assignment (Week 1 and 2).ipynb | ireneumoh24/ISM416 |
contiguous() is to arrange the tensor in a standard layout | a = torch.randn(3, 4, 5)
b = a.permute(1, 2, 0)
b_cont = b.contiguous()
a_cont = a.contiguous()
# a has "standard layout" (also known as C layout in numpy) descending strides, and no memory gaps (stride(i-1) == size(i)*stride(i))
print (a.shape, a.stride(), a.data_ptr())
# b has same storage as a (data_ptr), but has th... | _____no_output_____ | MIT | mywork/Session 6 - GRU Language Model.ipynb | mingsqtt/textanalytics_ml |
class NodoArbol:
def __init__(self, dato, hijo_izq = None, hijo_der = None):
self.dato = dato
self.left = hijo_izq
self.right = hijo_der
class BinarySearchTree:
def __init__(self):
self.__root = None
def insert(self, value):
if self.__root == None:
self.__root = NodoArbol(value, None,... | _____no_output_____ | MIT | Tarea26.ipynb | Ed-10/Daa_2021_1 | |
window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-59152712-8'); Explicit Runge Kutta methods and their Butcher tables Authors: Brandon Clark & Zach Etienne This tutorial notebook stores known explicit Runge Kutta-like methods as Butche... | # Step 1: Initialize needed Python modules
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2: The Family of Explicit Runge-Kutta-Like Schemes (Butcher Tables) [Back to [top](toc)\]$$\label{introbutcher}$$In general, a predictor-corrector method performs an estimate timestep from $n$ to $n+1$, using e.g., a Runge Kutta method, to get a prediction of the solution at timestep $n+1$. This is the "predictor... | # Step 2a: Generating a Dictionary of Butcher Tables for Explicit Runge Kutta Techniques
# Initialize the dictionary Butcher_dict
Butcher_dict = {} | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.i: Euler's Method [Back to [top](toc)\]$$\label{euler}$$[Forward Euler's method](https://en.wikipedia.org/w/index.php?title=Euler_method&oldid=896152463) is a first order Runge Kutta method. Euler's method obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:$$y_{n+1} = y_{n} + \Delta tf(y_{n}, t_{n}... | # Step 2.a.i: Euler's Method
Butcher_dict['Euler'] = (
[[sp.sympify(0)],
["", sp.sympify(1)]]
, 1) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.ii: RK2 Heun's Method [Back to [top](toc)\]$$\label{rktwoheun}$$[Heun's method](https://en.wikipedia.org/w/index.php?title=Heun%27s_method&oldid=866896936) is a second-order RK method that obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begin{align}k_1 &= \Delta tf(y_n, t_n), \\k_2 &= \Delta tf(... | # Step 2.a.ii: RK2 Heun's Method
Butcher_dict['RK2 Heun'] = (
[[sp.sympify(0)],
[sp.sympify(1), sp.sympify(1)],
["", sp.Rational(1,2), sp.Rational(1,2)]]
, 2) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.iii: RK2 Midpoint Method [Back to [top](toc)\]$$\label{rk2mp}$$[Midpoint method](https://en.wikipedia.org/w/index.php?title=Midpoint_method&oldid=886630580) is a second-order RK method that obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begin{align}k_1 &= \Delta tf(y_n, t_n), \\k_2 &= \Delta tf... | # Step 2.a.iii: RK2 Midpoint (MP) Method
Butcher_dict['RK2 MP'] = (
[[sp.sympify(0)],
[sp.Rational(1,2), sp.Rational(1,2)],
["", sp.sympify(0), sp.sympify(1)]]
, 2) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.iv: RK2 Ralston's Method [Back to [top](toc)\]$$\label{rk2ralston}$$Ralston's method (see [Ralston (1962)](https://www.ams.org/journals/mcom/1962-16-080/S0025-5718-1962-0150954-0/S0025-5718-1962-0150954-0.pdf), is a second-order RK method that obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begi... | # Step 2.a.iv: RK2 Ralston's Method
Butcher_dict['RK2 Ralston'] = (
[[sp.sympify(0)],
[sp.Rational(2,3), sp.Rational(2,3)],
["", sp.Rational(1,4), sp.Rational(3,4)]]
, 2)
| _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.v: Kutta's Third-order Method [Back to [top](toc)\]$$\label{rk3}$$[Kutta's third-order method](https://en.wikipedia.org/w/index.php?title=List_of_Runge%E2%80%93Kutta_methods&oldid=896594269) obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begin{align}k_1 &= \Delta tf(y_n, t_n), \\k_2 &= \Delta ... | # Step 2.a.v: Kutta's Third-order Method
Butcher_dict['RK3'] = (
[[sp.sympify(0)],
[sp.Rational(1,2), sp.Rational(1,2)],
[sp.sympify(1), sp.sympify(-1), sp.sympify(2)],
["", sp.Rational(1,6), sp.Rational(2,3), sp.Rational(1,6)]]
, 3) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.vi: RK3 Heun's Method [Back to [top](toc)\]$$\label{rk3heun}$$[Heun's third-order method](https://en.wikipedia.org/w/index.php?title=List_of_Runge%E2%80%93Kutta_methods&oldid=896594269) obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begin{align}k_1 &= \Delta tf(y_n, t_n), \\k_2 &= \Delta tf(y_n... | # Step 2.a.vi: RK3 Heun's Method
Butcher_dict['RK3 Heun'] = (
[[sp.sympify(0)],
[sp.Rational(1,3), sp.Rational(1,3)],
[sp.Rational(2,3), sp.sympify(0), sp.Rational(2,3)],
["", sp.Rational(1,4), sp.sympify(0), sp.Rational(3,4)]]
, 3) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.vii: RK3 Ralton's Method [Back to [top](toc)\]$$\label{rk3ralston}$$Ralston's third-order method (see [Ralston (1962)](https://www.ams.org/journals/mcom/1962-16-080/S0025-5718-1962-0150954-0/S0025-5718-1962-0150954-0.pdf), obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begin{align}k_1 &= \Delta... | # Step 2.a.vii: RK3 Ralton's Method
Butcher_dict['RK3 Ralston'] = (
[[0],
[sp.Rational(1,2), sp.Rational(1,2)],
[sp.Rational(3,4), sp.sympify(0), sp.Rational(3,4)],
["", sp.Rational(2,9), sp.Rational(1,3), sp.Rational(4,9)]]
, 3) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.viii: Strong Stability Preserving Runge-Kutta (SSPRK3) Method [Back to [top](toc)\]$\label{ssprk3}$The [Strong Stability Preserving Runge-Kutta (SSPRK3)](https://en.wikipedia.org/wiki/List_of_Runge%E2%80%93Kutta_methodsKutta's_third-order_method) method obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$... | # Step 2.a.viii: Strong Stability Preserving Runge-Kutta (SSPRK3) Method
Butcher_dict['SSPRK3'] = (
[[0],
[sp.sympify(1), sp.sympify(1)],
[sp.Rational(1,2), sp.Rational(1,4), sp.Rational(1,4)],
["", sp.Rational(1,6), sp.Rational(1,6), sp.Rational(2,3)]]
, 3) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.ix: Classic RK4 Method [Back to [top](toc)\]$$\label{rkfour}$$The [classic RK4 method](https://en.wikipedia.org/w/index.php?title=Runge%E2%80%93Kutta_methods&oldid=894771467) obtains the solution $y(t)$ at time $t_{n+1}$ from $t_n$ via:\begin{align}k_1 &= \Delta tf(y_n, t_n), \\k_2 &= \Delta tf(y_n + \frac{1}... | # Step 2.a.vix: Classic RK4 Method
Butcher_dict['RK4'] = (
[[sp.sympify(0)],
[sp.Rational(1,2), sp.Rational(1,2)],
[sp.Rational(1,2), sp.sympify(0), sp.Rational(1,2)],
[sp.sympify(1), sp.sympify(0), sp.sympify(0), sp.sympify(1)],
["", sp.Rational(1,6), sp.Rational(1,3), sp.Rational(1,3), sp.Rational(1,6)]]
, 4) | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.x: RK5 Dormand-Prince Method [Back to [top](toc)\]$$\label{dp5}$$The fifth-order Dormand-Prince (DP) method from the RK5(4) family (see [Dormand, J. R.; Prince, P. J. (1980)](https://www.sciencedirect.com/science/article/pii/0771050X80900133?via%3Dihub)) Butcher table is:$$\begin{array}{c|ccccccc} 0 & \\ ... | # Step 2.a.x: RK5 Dormand-Prince Method
Butcher_dict['DP5'] = (
[[0],
[sp.Rational(1,5), sp.Rational(1,5)],
[sp.Rational(3,10),sp.Rational(3,40), sp.Rational(9,40)],
[sp.Rational(4,5), sp.Rational(44,45), sp.Rational(-56,15), sp.Rational(32,9)],
[sp.Rational(8,9), sp.Rational(19372,6561), sp.Rational(-25360,2187), sp... | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.xi: RK5 Dormand-Prince Method Alternative [Back to [top](toc)\]$$\label{dp5alt}$$The fifth-order Dormand-Prince (DP) method from the RK6(5) family (see [Dormand, J. R.; Prince, P. J. (1981)](https://www.sciencedirect.com/science/article/pii/0771050X81900103)) Butcher table is:$$\begin{array}{c|ccccccc} 0 ... | # Step 2.a.xi: RK5 Dormand-Prince Method Alternative
Butcher_dict['DP5alt'] = (
[[0],
[sp.Rational(1,10), sp.Rational(1,10)],
[sp.Rational(2,9), sp.Rational(-2, 81), sp.Rational(20, 81)],
[sp.Rational(3,7), sp.Rational(615, 1372), sp.Rational(-270, 343), sp.Rational(1053, 1372)],
[sp.Rational(3,5), sp.Rational(3243, ... | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.xii: RK5 Cash-Karp Method [Back to [top](toc)\]$$\label{ck5}$$The fifth-order Cash-Karp Method (see [J. R. Cash, A. H. Karp. (1980)](https://dl.acm.org/citation.cfm?doid=79505.79507)) Butcher table is:$$\begin{array}{c|cccccc} 0 & \\ \frac{1}{5} & \frac{1}{5} & \\ \frac{3}{10} & \frac{3}{40} & \frac{9}{... | # Step 2.a.xii: RK5 Cash-Karp Method
Butcher_dict['CK5'] = (
[[0],
[sp.Rational(1,5), sp.Rational(1,5)],
[sp.Rational(3,10),sp.Rational(3,40), sp.Rational(9,40)],
[sp.Rational(3,5), sp.Rational(3,10), sp.Rational(-9,10), sp.Rational(6,5)],
[sp.sympify(1), sp.Rational(-11,54), sp.Rational(5,2), sp.Rational(-70,27), sp... | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.xiii: RK6 Dormand-Prince Method [Back to [top](toc)\]$$\label{dp6}$$The sixth-order Dormand-Prince method (see [Dormand, J. R.; Prince, P. J. (1981)](https://www.sciencedirect.com/science/article/pii/0771050X81900103)) Butcher Table is$$\begin{array}{c|cccccccc} 0 & \\ \frac{1}{10} & \frac{1}{10} & \\ ... | # Step 2.a.xiii: RK6 Dormand-Prince Method
Butcher_dict['DP6'] = (
[[0],
[sp.Rational(1,10), sp.Rational(1,10)],
[sp.Rational(2,9), sp.Rational(-2, 81), sp.Rational(20, 81)],
[sp.Rational(3,7), sp.Rational(615, 1372), sp.Rational(-270, 343), sp.Rational(1053, 1372)],
[sp.Rational(3,5), sp.Rational(3243, 5500), sp.Rat... | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.xiv: RK6 Luther's Method [Back to [top](toc)\]$$\label{l6}$$Luther's sixth-order method (see [H. A. Luther (1968)](http://www.ams.org/journals/mcom/1968-22-102/S0025-5718-68-99876-1/S0025-5718-68-99876-1.pdf)) Butcher table is:$$\begin{array}{c|ccccccc} 0 & \\ 1 & 1 & \\ \frac{1}{2} & \frac{3}{8} & ... | # Step 2.a.xiv: RK6 Luther's Method
q = sp.sqrt(21)
Butcher_dict['L6'] = (
[[0],
[sp.sympify(1), sp.sympify(1)],
[sp.Rational(1,2), sp.Rational(3,8), sp.Rational(1,8)],
[sp.Rational(2,3), sp.Rational(8,27), sp.Rational(2,27), sp.Rational(8,27)],
[(7 - q)/14, (-21 + 9*q)/392, (-56 + 8*q)/392, (336 -48*q)/392, (-63 + ... | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 2.a.xv: RK8 Dormand-Prince Method [Back to [top](toc)\]$$\label{dp8}$$The eighth-order Dormand-Prince Method (see [Dormand, J. R.; Prince, P. J. (1981)](https://www.sciencedirect.com/science/article/pii/0771050X81900103)) Butcher table is:$$\begin{array}{c|ccccccccc} 0 & \\ \frac{1}{18} & \frac{1}{18} & \\... | # Step 2.a.xv: RK8 Dormand-Prince Method
Butcher_dict['DP8']=(
[[0],
[sp.Rational(1, 18), sp.Rational(1, 18)],
[sp.Rational(1, 12), sp.Rational(1, 48), sp.Rational(1, 16)],
[sp.Rational(1, 8), sp.Rational(1, 32), sp.sympify(0), sp.Rational(3, 32)],
[sp.Rational(5, 16), sp.Rational(5, 16), sp.sympify(0), sp.Rational(-... | _____no_output_____ | BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 3: Code validation against `MoLtimestepping.RK_Butcher_Table_Dictionary` NRPy+ module [Back to [top](toc)\]$$\label{code_validation}$$As a code validation check, we verify agreement in the dictionary of Butcher tables between1. this tutorial and 2. the NRPy+ [MoLtimestepping.RK_Butcher_Table_Dictionary](../edit/M... | # Step 3: Code validation against MoLtimestepping.RK_Butcher_Table_Dictionary NRPy+ module
import sys # Standard Python module for multiplatform OS-level functions
from MoLtimestepping.RK_Butcher_Table_Dictionary import Butcher_dict as B_dict
valid = True
for key, value in Butcher_dict.items():
if Butcher_dict[key... | The dictionaries match!
| BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Step 4: Output this notebook to $\LaTeX$-formatted PDF file \[Back to [top](toc)\]$$\label{latex_pdf_output}$$The following code cell converts this Jupyter notebook into a proper, clickable $\LaTeX$-formatted PDF file. After the cell is successfully run, the generated PDF may be found in the root NRPy+ tutorial direct... | import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface
cmd.output_Jupyter_notebook_to_LaTeXed_PDF("Tutorial-RK_Butcher_Table_Dictionary") | Created Tutorial-RK_Butcher_Table_Dictionary.tex, and compiled LaTeX file
to PDF file Tutorial-RK_Butcher_Table_Dictionary.pdf
| BSD-2-Clause | Tutorial-RK_Butcher_Table_Dictionary.ipynb | stevenrbrandt/nrpytutorial |
Load Audio files | al_train = AudioLoader(directory='../data/train')
# al_test = AudioLoader(directory="../data/test",tts_file=r'/trsTest.txt')
df_train_audio_data = al_train.get_audio_info_with_data()
# df_test_audio_data = al_test.get_audio_info_with_data()
# rp = ResultPickler()
# rp.load_data("../models/LoadedAudioInfo.pkl")
# data_... | _____no_output_____ | MIT | notebooks/AudioManipulation.ipynb | DePacifier/AMH-STT |
Preprocessing the audio Data- change the duration to the same size- convert channels to stereo by duplicating the other channel- standardize the sampling rate to the same one- Data Augmentation- Extract Features Convert Channels to Stereo by duplicating the other channel | am_train.convert_stereo_audio()
am_train.get_audio_info()
# am_train.get_audio_info().head().loc[0,"TimeSeriesData"].shape
num_rows, y_len = am_train.get_audio_info().loc[0,"TimeSeriesData"].shape
num_rows,y_len | _____no_output_____ | MIT | notebooks/AudioManipulation.ipynb | DePacifier/AMH-STT |
Change the duration to the same sizeFrom Our Data Exploration, we found that most frequent audio files has a duration between 2 to 6. And to reduce the bias, we will pad all the audio to make it equal in length with the maximum. | am_train.resize_audio()
am_train.get_audio_info()
am_train.get_audio_info().loc[0,"TimeSeriesData"].shape | _____no_output_____ | MIT | notebooks/AudioManipulation.ipynb | DePacifier/AMH-STT |
Standardize Sampling Rate | # count sampling rate frequencies
pd.DataFrame({"count": df_train_audio_data.groupby("SamplingRate")["SamplingRate"].count()})
am_train.resample_audio()
am_train.get_audio_info() | _____no_output_____ | MIT | notebooks/AudioManipulation.ipynb | DePacifier/AMH-STT |
Our SamplingRate is the same all around our data but we have resampled it to 44100. Now we have our processed data, we will save the preprocessed files to a folder in a .wav format. | am_train.write_wave_files("../data/train/wav/") | _____no_output_____ | MIT | notebooks/AudioManipulation.ipynb | DePacifier/AMH-STT |
Augmentation Feature Extraction We can now extract features but first we convert back to mono since the librosa library expects a monochannel audio. |
# features = am_train.extract_features()
# features.head()
# vis.plot_spectrogram(features.loc[0,'Melspectogram'])
# vis.plot_spectrogram(features.loc[0,'Melspectogram_db']) | _____no_output_____ | MIT | notebooks/AudioManipulation.ipynb | DePacifier/AMH-STT |
Fictitious Names Introduction:This time you will create a data again Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.All the credits to this exercise belongs to him. In order to understand about it go to [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-jo... | import pandas as pd | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 2. Create the 3 DataFrames based on the followin raw data | raw_data_1 = {
'subject_id': ['1', '2', '3', '4', '5'],
'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}
raw_data_2 = {
'subject_id': ['4', '5', '6', '7', '8'],
'first_name': ['Billy', 'Brian', 'Bran', '... | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 3. Assign each to a variable called data1, data2, data3 |
data3 | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 4. Join the two dataframes along rows and assign all_data | all_data = pd.concat([data1, data2])
all_data | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 5. Join the two dataframes along columns and assing to all_data_col | all_data_col = pd.concat([data1, data2], axis = 1)
all_data_col | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 6. Print data3 | data3 | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 7. Merge all_data and data3 along the subject_id value | pd.merge(all_data, data3, on='subject_id') | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2 | pd.merge(data1, data2, on='subject_id', how='inner') | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
Step 9. Merge all values in data1 and data2, with matching records from both sides where available. | pd.merge(data1, data2, on='subject_id', how='outer') | _____no_output_____ | BSD-3-Clause | 05_Merge/Fictitous Names/Exercises_with_solutions.ipynb | ktats/pandas_exercises |
In this example, we will compare development of a pairs trading strategy using backtrader and vectorbt. | import numpy as np
import pandas as pd
import datetime
import collections
import math
import pytz
import scipy.stats as st
SYMBOL1 = 'PEP'
SYMBOL2 = 'KO'
FROMDATE = datetime.datetime(2017, 1, 1, tzinfo=pytz.utc)
TODATE = datetime.datetime(2019, 1, 1, tzinfo=pytz.utc)
PERIOD = 100
CASH = 100000
COMMPERC = 0.005 # 0.5%... | _____no_output_____ | Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
Data | import vectorbt as vbt
start_date = FROMDATE.replace(tzinfo=pytz.utc)
end_date = TODATE.replace(tzinfo=pytz.utc)
data = vbt.YFData.download([SYMBOL1, SYMBOL2], start=start_date, end=end_date)
data = data.loc[(data.wrapper.index >= start_date) & (data.wrapper.index < end_date)]
print(data.data[SYMBOL1].iloc[[0, -1]])
p... | Open High Low Close \
Date
2017-01-03 00:00:00+00:00 91.831129 91.962386 91.192316 91.577354
2018-12-31 00:00:00+00:00 102.775161 103.249160 101.604091 102.682220
... | Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
backtrader Adapted version of https://github.com/mementum/backtrader/blob/master/contrib/samples/pair-trading/pair-trading.py | import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind
class CommInfoFloat(bt.CommInfoBase):
"""Commission schema that keeps size as float."""
params = (
('stocklike', True),
('commtype', bt.CommInfoBase.COMM_PERC),
('percabs', True),
)
... | 1.07 s ± 16.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
| Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
vectorbt Using Portfolio.from_orders | from numba import njit
@njit
def rolling_logret_zscore_nb(a, b, period):
"""Calculate the log return spread."""
spread = np.full_like(a, np.nan, dtype=np.float_)
spread[1:] = np.log(a[1:] / a[:-1]) - np.log(b[1:] / b[:-1])
zscore = np.full_like(a, np.nan, dtype=np.float_)
for i in range(a.shape[0])... | 3.04 ms ± 5.31 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
| Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
While Portfolio.from_orders is a very convenient and optimized function for simulating portfolios, it requires some prior steps to produce the size array. In the example above, we needed to manually run the calculation of the spread z-score, generate the signals from the z-score, build the size array from the signals, ... | from vectorbt.portfolio import nb as portfolio_nb
from vectorbt.base.reshape_fns import flex_select_auto_nb
from vectorbt.portfolio.enums import SizeType, Direction
from collections import namedtuple
Memory = namedtuple("Memory", ('spread', 'zscore', 'status'))
Params = namedtuple("Params", ('period', 'upper', 'lower'... | 4.44 ms ± 20.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
| Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
Numba paradise (or hell?) - fastest | def simulate_nb_from_order_func():
"""Simulate using `simulate_nb`."""
# iterate over 502 rows and 2 columns, each element is a potential order
target_shape = vbt_close_price.shape
# number of columns in the group - exactly two
group_lens = np.array([2])
# build default call sequence (... | 2.24 ms ± 3.67 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
| Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
As you can see, writing Numba isn't straightforward and requires at least intermediate knowledge of NumPy. That's why Portfolio.from_orders and other class methods based on arrays are usually a good starting point. Multiple parameters Now, why waste all energy to port a strategy to vectorbt? Right, for hyperparameter ... | periods = np.arange(10, 105, 5)
uppers = np.arange(1.5, 2.2, 0.1)
lowers = -1 * np.arange(1.5, 2.2, 0.1)
def simulate_mult_from_order_func(periods, uppers, lowers):
"""Simulate multiple parameter combinations using `Portfolio.from_order_func`."""
# Build param grid
param_product = vbt.utils.params.create_pa... | 2.16 s ± 16.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
| Apache-2.0 | examples/PairsTrading.ipynb | zhnagchulan/vectorbt |
Predictions | '''preds = reg.predict_proba(X_test)
preds_valid = clf.predict_proba(X_valid)
print(f"BEST VALID SCORE FOR {dataset_name} : {clf.best_cost}")
print(f"FINAL TEST SCORE FOR {dataset_name} : {test_auc}")''' | _____no_output_____ | MIT | stock_fit.ipynb | LiziCyber/tabnet |
Save and load Model | # save tabnet model
saving_path_name = "./tabnet_model_test_1"
saved_filepath = reg.save_model(saving_path_name)
# define new model with basic parameters and load state dict weights
loaded_reg = TabNetRegressor()
loaded_reg.load_model(saved_filepath) | _____no_output_____ | MIT | stock_fit.ipynb | LiziCyber/tabnet |
Global explainability : feat importance summing to 1 | reg.feature_importances_ | _____no_output_____ | MIT | stock_fit.ipynb | LiziCyber/tabnet |
Local explainability and masks | explain_matrix, masks = reg.explain(X_valid)
fig, axs = plt.subplots(1, 3, figsize=(20,20))
for i in range(3):
axs[i].imshow(masks[i][:50])
axs[i].set_title(f"mask {i}")
| _____no_output_____ | MIT | stock_fit.ipynb | LiziCyber/tabnet |
Modeling and Simulation in PythonChapter 1Copyright 2017 Allen DowneyLicense: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0) JupyterWelcome to Modeling and Simulation, welcome to Python, and welcome to Jupyter.This is a Jupyter notebook, which is a development environmen... | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim library
from modsim import *
print('If this cell runs successfully, i... | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
The first time you run this on a new installation of Python, it might produce a warning message in pink. That's probably ok, but if you get a message that says `modsim.py depends on Python 3.7 features`, that means you have an older version of Python, and some features in `modsim.py` won't work correctly.If you need a... | meter = UNITS.meter
second = UNITS.second | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
To find out what other units are defined, type `UNITS.` (including the period) in the next cell and then press TAB. You should see a pop-up menu with a list of units. Create a variable named `a` and give it the value of acceleration due to gravity. | a = 9.8 * meter / second**2 | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
Create `t` and give it the value 4 seconds. | t = 4 * second | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
Compute the distance a penny would fall after `t` seconds with constant acceleration `a`. Notice that the units of the result are correct. | a * t**2 / 2 | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
**Exercise**: Compute the velocity of the penny after `t` seconds. Check that the units of the result are correct. | # Solution goes here | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
**Exercise**: Why would it be nonsensical to add `a` and `t`? What happens if you try? | # Solution goes here | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
The error messages you get from Python are big and scary, but if you read them carefully, they contain a lot of useful information.1. Start from the bottom and read up.2. The last line usually tells you what type of error happened, and sometimes additional information.3. The previous lines are a "traceback" of what ... | h = 381 * meter | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
Compute the time it would take a penny to fall, assuming constant acceleration.$ a t^2 / 2 = h $$ t = \sqrt{2 h / a}$ | t = sqrt(2 * h / a) | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
Given `t`, we can compute the velocity of the penny when it lands.$v = a t$ | v = a * t | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
We can convert from one set of units to another like this: | mile = UNITS.mile
hour= UNITS.hour
v.to(mile/hour) | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
**Exercise:** Suppose you bring a 10 foot pole to the top of the Empire State Building and use it to drop the penny from `h` plus 10 feet.Define a variable named `foot` that contains the unit `foot` provided by `UNITS`. Define a variable named `pole_height` and give it the value 10 feet.What happens if you add `h`, wh... | # Solution goes here
# Solution goes here | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
**Exercise:** In reality, air resistance limits the velocity of the penny. At about 18 m/s, the force of air resistance equals the force of gravity and the penny stops accelerating.As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 18 m/s, and then 0 afterwards. What i... | # Solution goes here
# Solution goes here
# Solution goes here
# Solution goes here
# Solution goes here | _____no_output_____ | MIT | notebooks/chap01.ipynb | erhardt/ModSimPy |
Global Imports | %pylab inline | Populating the interactive namespace from numpy and matplotlib
| MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
External Package Imports | import os as os
import pickle as pickle
import pandas as pd | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
Module Imports | from Stats.Scipy import *
from Stats.Survival import *
from Helpers.Pandas import *
from Figures.FigureHelpers import *
from Figures.Pandas import *
from Figures.Boxplots import *
from Figures.Survival import draw_survival_curve, survival_and_stats
from Figures.Survival import draw_survival_curves
from Figures.Surviva... | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
Tweaking Display Parameters | pd.set_option('precision', 3)
pd.set_option('display.width', 300)
plt.rcParams['font.size'] = 12
'''Color schemes for paper taken from http://colorbrewer2.org/'''
colors = plt.rcParams['axes.color_cycle']
colors_st = ['#CA0020', '#F4A582', '#92C5DE', '#0571B0']
colors_th = ['#E66101', '#FDB863', '#B2ABD2', '#5E3C99'] | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
Function to Pull a Firehose Run Container | def get_run(firehose_dir, version='Latest'):
'''
Helper to get a run from the file-system.
'''
path = '{}/ucsd_analyses'.format(firehose_dir)
if version is 'Latest':
version = sorted(os.listdir(path))[-1]
run = pickle.load(open('{}/{}/RunObject.p'.format(path, version), 'rb'))
retur... | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
Read In Data Here we read in the pre-processed data that we downloaded and initialized in the [download_data notebook](download_data.ipynb). | print 'populating namespace with data'
OUT_PATH = '/cellar/users/agross/TCGA_Code/CancerData/Data'
RUN_DATE = '2014_07_15'
VERSION = 'all'
CANCER = 'HNSC'
FIGDIR = '../Figures/'
if not os.path.isdir(FIGDIR):
os.makedirs(FIGDIR)
run_path = '{}/Firehose__{}/'.format(OUT_PATH, RUN_DATE)
run = get_run(run_path, 'Run_'... | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
Update Clinical Data | from Processing.ProcessClinicalDataPortal import update_clinical_object
p = '/cellar/users/agross/TCGA_Code/TCGA/Data'
path = p + '/Followup_R9/HNSC/'
clinical = update_clinical_object(clinical, path)
clinical.clinical.shape
#hpv = clinical.hpv
surv = clinical.survival.survival_5y
age = clinical.clinical.age.astype(f... | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
HPV Data | p = '/cellar/users/agross/TCGA_Code/TCGA/'
hpv_all = pd.read_csv(p + '/Extra_Data/hpv_summary_3_20_13_distribute.csv', index_col=0)
hpv = hpv_all.Molecular_HPV.map({0:'HPV-', 1:'HPV+'})
hpv.name = 'HPV'
hpv_seq = hpv
status = clinical.clinical[['hpvstatusbyishtesting','hpvstatusbyp16testing']]
status = status.replace('... | _____no_output_____ | MIT | Notebooks/HNSCC_Imports.ipynb | theandygross/CancerData |
این دفترچه تنها جهت تمرین در کنار مطالعهی درسنامه اضافه شده است. | import numpy as np
i = np.identity(3)
b = i == 0
print(i[b].shape)
x = np.full((3, 3), 8)
print(np.dot(i, x)) | [[8. 8. 8.]
[8. 8. 8.]
[8. 8. 8.]]
| MIT | quera/13609/46444/solution.ipynb | TheMn/Quera-College-ML-Course |
3 - Displaying Histograms and Crossplots Created by: Andy McDonald The following tutorial illustrates how to display well data from a LAS file on histograms and crossplots. Loading Well Data from CSV The following cells load data in from a CSV file and replace the null values (-999.25) with Not a Number (NaN) values.... | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
root = '/users/kai/desktop/data_science/data/dongara'
well_name = 'dongara_20'
file_format = '.csv'
well = pd.read_csv(os.path.join(root,well_name+file_format), header=0)
well.replace(-999.25, np.nan, inplace=True)
cols = well.columns[well... | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
Displaying data on a histogram Displaying a simple histogram can be done by calling the .hist function on the well dataframe and specifying the column. | well.hist(column="GR") | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
The number of bins can be controled by the bins parameter: | well.hist(column="GR", bins = 30) | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
We can also change the opacity of the bars by using the alpha parameter: | well.hist(column="GR", bins = 30, alpha = 0.5) | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
Plotting multiple histograms on one plot It can be more efficient to loop over the columns (curves) within the dataframe and create a plot with multiple histograms, rather than duplicating the previous line multiple times. First we need to create a list of our curve names. | cols_to_plot = list(well) | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
We can remove the depth curve from our list and focus on our curves. The same line can be applied to other curves that need removing. | cols_to_plot.remove("DEPT")
#Setup the number of rows and columns for our plot
rows = 5
cols = 2
fig=plt.figure(figsize=(10,10))
for i, feature in enumerate(cols_to_plot):
ax=fig.add_subplot(rows,cols,i+1)
well[feature].hist(bins=20,ax=ax,facecolor='green', alpha=0.6)
ax.set_title(feature+" Distribution")... | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
Displaying data on a crossplot (Scatterplot) As seen in the first notebook, we can display a crossplot by simply doing the following. using the c argument we can add a third curve to colour the data. | well.plot(kind="scatter", x="NPHI", y="RHOB", c="GR",
colormap="YlOrRd_r", ylim=(3,2))
| _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
We can take the above crossplot and create a 3D version. First we need to make sure the Jupyter notbook is setup for displaying interactive 3D plots using the following command. | %matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111, projection="3d")
ax.scatter(well["NPHI"], well["RHOB"], well["GR"], alpha= 0.3, c="r") | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
If we want to have multiple crossplots on view, we can do this by: | fig, ax = plt.subplots(figsize=(10,10))
ax1 = plt.subplot2grid((2,2), (0,0), rowspan=1, colspan=1)
ax2 = plt.subplot2grid((2,2), (0,1), rowspan=1, colspan=1)
ax3 = plt.subplot2grid((2,2), (1,0), rowspan=1, colspan=1)
ax4 = plt.subplot2grid((2,2), (1,1), rowspan=1, colspan=1)
ax1.scatter(x= "NPHI", y= "RHOB", data= we... | _____no_output_____ | MIT | 03 - Displaying Histograms and Crossplots.ipynb | will6309/Petrophysics-Python-Series |
Ranking multiple systemsIn this notebook, we consider the situation where we have scores from multiple different automated scoring systems, each with different levels of performance. We evaluate these systems against the same as well as different pairs of raters and show that:1. When using the same pair of raters to ... | import itertools
import json
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from pathlib import Path
from rsmtool.utils.prmse import prmse_true
from simulation.dataset import Dataset
from simulation.utils import (compute_agreement_one_system_one_rater_pair,
... | _____no_output_____ | MIT | notebooks/ranking_multiple_systems.ipynb | EducationalTestingService/prmse-simulations |
Step 1: SetupTo set up the experiment, we first load the dataset we have already created and saved in the `making_a_dataset.ipynb` notebook and use that for this experiment.For convenience and replicability, we have pre-defined many of the parameters that are used in our notebooks and saved them in the file `settings.... | # load the dataset file
dataset = Dataset.from_file('../data/default.dataset')
# let's remind ourselves what the dataset looks like
print(dataset)
# load the experimental settings file
experiment_settings = json.load(open('settings.json', 'r'))
# now get the data frames for our loaded dataset
df_scores, df_rater_metad... | _____no_output_____ | MIT | notebooks/ranking_multiple_systems.ipynb | EducationalTestingService/prmse-simulations |
Step 2: Evaluate all systems against same pair of ratersFirst, we evaluate the scores assigned by all our simulated systems in the dataset against the same pair of simulated human raters from the dataset. To simulate the more usual scenario, we sample two raters from the "average" rater category. | # define our pre-selected rater category
chosen_rater_category = "average"
# get the list of rater IDs in this category
rater_ids = df_rater_metadata[df_rater_metadata['rater_category'] == chosen_rater_category]['rater_id']
# choose 2 rater IDs randomly from these
chosen_rater_pair = rater_id1, rater_id2 = rater_ids.... | we chose the rater pair: ['h_107', 'h_101']
| MIT | notebooks/ranking_multiple_systems.ipynb | EducationalTestingService/prmse-simulations |
Now, we compute the agreement metrics as well as the PRMSE values for all of the simulated systems in our dataset against our pre-selected rater pair. | # initialize some lists that will hold our metric and PRMSE values for each category
metric_dfs = []
prmse_series = []
# iterate over each system category
for system_category in dataset.system_categories:
# get the system IDs that belong to this system category
system_ids_for_category = df_system_metadata... | _____no_output_____ | MIT | notebooks/ranking_multiple_systems.ipynb | EducationalTestingService/prmse-simulations |
Now, we need to use each metric to compute the ranks of each of the systems based on the values. | # compute the ranks given the metric values
df_ranks_same_rater_pair = compute_ranks_from_metrics(df_metrics_same_rater_pair_with_categories)
# now compute a longer verssion of this rank data frame that is more amenable to plotting
df_ranks_same_rater_pair_long = df_ranks_same_rater_pair.melt(id_vars=['system_category... | _____no_output_____ | MIT | notebooks/ranking_multiple_systems.ipynb | EducationalTestingService/prmse-simulations |
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