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twitter announced today that it will be removing its implementation of stories dubbed “fleets.” the feature was either loved or hated by twitter users since its initial release last year.

this short-lived feature, which was released in november of last year, will be removed on august 3. twitter acknowledged the controversial nature of the snapchat/instagram clone with the farewell tweet. notably, there was no fleet from the main twitter account announcing the departure of the feature, only a standard tweet.

in the goodbye, the company said it is working on “new stuff.” one can hope that they add the ability to edit tweets, in addition to the new edit audience and monetization features.

in a more detailed blog post, twitter shared that it hoped fleets would make people more comfortable posting onto twitter. as fleets disappear, some of the fleet creation features, like gifs and stickers, will be implemented into the standard tweets composer.

ftc: we use income earning auto affiliate links.more.


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Developing Scorecards in Python using OptBinning

Scorecards are risk models used by lending businesses to evaluate customers trying to access credit. A well-developed scorecard brings a lot of value to financial institutions and is essential for making decisions in terms of credit policy. Even though the mathematics and logic behind a scorecard are not complex, developing a well-performing model can be hard, as it takes a lot of time to organize and process data.

The traditional approach is to separate the variables in numerical or categorical and apply a binning approach to group values that show a similar relationship with the target (usually binary) using the Weight of Evidence of each value. This process of binning can be time-consuming and imperfect, as some decisions on if to merge bins can be judgmental and influenced by the scorecard developer. That’s one of the reasons banks and other institutions can take several months to develop or re-train scorecard models.

OptBinning tries to fill the gap between reliability in binning features and scorecard development, and flexibility in terms of having a library written in Python (a widely used language for data analytics).

“OptBinning is a library written in Python implementing a rigorous and flexible mathematical programming formulation for solving the optimal binning problem for a binary, continuous or multiclass target type, incorporating constraints not previously addressed”.

More than just offering a powerful method for performing binning, OptBinning also provides a wide variety of tools needed to select features, create scorecards, and visualize the performance during the development process.

OptBinning uses Scikit-Learn as the structure of its binning classes, making it intuitive to use, with and methods, just like any Scikit-Learn estimator.

Binning is the process of dividing values of a continuous variable into groups that share a similar behavior in respect to a characteristic. This technique that discretizes values into buckets is extremely valuable for understanding the relationship between the feature and the target. Binning is an essential step in Scorecard development, as each bin is associated with a Scorecard value, helping bring explainability to the model.

“From a modeling perspective, the binning technique may address prevalent data issues such as the handling of missing values, the presence of outliers and statistical noise, and data scaling.”

Optimal binning: mathematical programming formulation, Navas-Palencia G.

There are many available techniques for performing binning, and although some can be successfully implemented, there is no guarantee that they can reach the optimal bins. The optimal binning of a variable is the process where you discretize the samples in groups in order to satisfy a specific constraint while optimizing a divergence (or performance) metric. This constraint can be a specific number of bins or a minimum number of samples per bin.

OptBinning offers an efficient implementation of the optimal binning process, giving you control over parameters and constraints.

OptBinning has 3 main class types hierarchically related that perform all the processing needed to bin your features and create a Scorecard. The sessions below offer a brief description of how these classes are structured. For more detail please refer to OptBinning official documentation.

4.1. OptimalBinning, ContinuousOptimalBinning, and MulticlassOptimalBinning

is the base class for performing binning of a feature with a binary target. For continuous or multiclass targets two other classes are available: and .

As mentioned before, these 3 classes are built following structure with the and methods. Binning a feature using the mentioned classes is as simple as the code below:

# 1) Define your feature and target arrays
X = df_train['feat_name']
y = df_train['target']# 2) Instantiate class and fit to train dataset
optb = OptimalBinning(name='feat_name', dtype="numerical")
optb.fit(X, y)# 3) To perform the binning of a dataset
X_binned = optb.transform(X)# 4) To visualize the results table and plot
optb.binning_table.build()
optb.binning_table.plot(metric="woe")

As a default, the binning classes return the value of Weight of Evidence for the respective bin category. More parameters other than feature name and data type (numerical or categorical) are available, offering a considerable level of customization for this process.

4.2. BinningProcess

The class is built with the goal to perform optimal binning over a whole dataset, not just one feature as exemplified in the session above.

So the best way to view is as a wrapper for . The usage is fairly simple, with just a few parameters needed for performing the binning of a full dataset.

# 1) Define list of features and categorical ones
list_features = df_train.drop(columns=['TARGET']).columns.values
list_categorical = df_train.select_dtypes(include=['object', 'category']).columns.values# 2) Instantiate BinningProcess
binning_process = BinningProcess(
categorical_variables=list_categorical,
variable_names=list_features)# 3) Fit and transform dataset
df_train_binned = binning_process.fit_transform(df_train, y)

4.3. ScoreCard

The class offers the possibility of combining the binned dataset generated from a with a linear estimator from Scikit-Learn to generate a production-ready Scorecard.

# 1) Define a linear estimator (model)
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()# 2) Instatiate a ScoreCard and fit to dataset
scaling_method = "min_max"
scaling_method_data = {"min": 0, "max": 1000}
scorecard = Scorecard(
target='TARGET',
binning_process=binning_process,
estimator=logreg,
scaling_method=scaling_method,
scaling_method_params=scaling_method_data,
intercept_based=False,
reverse_scorecard=True,
)scorecard.fit(df_application_train)

So with just a few lines of code, you create a Scorecard model ready to be tested and put in production! The tutorial on how to create and validate a Scorecard is detailed in the next session.

Figure 1 summarizes the relationship of classes that are part of OptBinning.

To illustrate the process of creating a production-ready Scorecard with Optbinning we are going to use Kaggle’s Home Credit Risk Default dataset. You can find the Jupyter Notebook with the code on thetutorial repository.

5.1. Loading the dataset

After downloading the dataset files from Kaggle’s page and extracting the folder you’ll end up with a few CSV files. These files are part of Kaggle’s challenge description with information about features and tables. We are going to use the file to demonstrate OptBinning. After loading the dataset as a Pandas Dataframe we set the column as index (line 8) and split the dataset into train and test (line 11).

5.2. Exploring Features and BinningProcess

Feature engineering is one of the most important steps in any model development and Scorecards are no exception. As our focus is on demonstrating OptBinning usage for Scorecard development we won’t explore much of the possibilities engineering features from the dataset. Our approach is going to separate categorical from numerical features and define them when instantiating as the optimal binning process deals differently with these types of features. Another parameter we need to set at this stage is the , the constraints to use to define the optimal bins.

5.3. Choosing a linear estimator

One great feature present in OptBinning is the flexibility to choose any linear model (estimator) from Scikit_learn to use in your Scorecard. By tuning the parameters of the linear estimator you can increase the performance of your Scorecard. We are going to use Logistic Regression to illustrate the usage, but feel free to explore other estimators.

5.4. Creating the Scorecard

After instantiating a and a linear estimator you need to specify the scaling parameters (line 2 and 3) and pass them to your instance. Next, you run the fit method on your dataset (line 16) and that’s it! After a few seconds, your Scorecard is ready for performance validation.

5.5. Visualizing and validating the Scorecard

OptBinning offers a wide variety of methods for you to visualize and evaluate your Scorecard. You can access the Scorecard table with metrics for each binned feature and save it as CSV to document the model development.

Below you can see the part of the Scorecard table:

Finally, you can visualize your Scorecard performance using functions from module.

5.6. Using Scorecard in production

One of OptBinning's most incredible features is its capacity to be easily put into production. You can save your Scorecard model with pickle, store it and use it in production. To perform predictions you simply need to unpickle the Scorecard object to score samples from a Pandas Dataframe with the features you used to develop your model.

For many years, the development of scorecards was confined to large financial institutions that had the money to acquire expensive software tools like SAS. We have demonstrated OptBinning’s power and versatility for binning variables and creating scorecards.

Combining Optbinning with Python’s libraries for Data Science and Analytics (Pandas, Numpy, Matplotlib, Scikit-Learn) could offer all the tools needed to develop industry-level Scorecard models.

This could be a game-changer for small businesses and Fintechs as all these mentioned libraries are open-source, meaning the only investment these companies would need to do is on human resources.

Sours: https://towardsdatascience.com/developing-scorecards-in-python-using-optbinning-ab9a205e1f69
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Gabriel Allon

Height

Five feet, eight inches

Hair

Black, short, gray temples

Address

Narkis Street, Jerusalem

Books and Characters

This article is a stub. You can help the Gabriel Allon Wiki by expanding it.

Gabriel Allon was an artist until Munich. Afterward, he became an assassin for his country and is the current director of the Office.

Youth[]

Gabriel was born a Virgo in 1951, and grew up in the Jezreel Valley of Israel in the kibbutz of Ramat David. He was of German heritage[1] His mother was a survivor of the Nazi death camps. According to information in The Black Widow, Gabriel's father was "a man from Munich, a writer, an intellectual, who escaped to Palestine before the war. In Germany his name had been Greenberg, but in Israel he took the Hebrew name Allon." Gabriel's parents spoke a variety of European languages, but disliked speaking Hebrew. They treated him less like a child than a small adult. Tziona Levin, a family friend, was the closest thing he had to a sibling. His father died in the six-day war and his mother died from cancer some time later. Gabriel performed his mandatory service in the Israeli Defense Forces and later enrolled in the Betsal'el School of Art in Jerusalem. He was in school at the time of the Munich Massacre.

Wrath of God[]

Following the Munich massacre, Ari Shamron recruited Gabriel as an assassin in September 1972 to punish the members of the Black September movement. Of twelve members of Black September who were killed, Gabriel killed six with a .22-caliber Beretta pistol. Whenever he could, he shot his victims eleven times, one for each Jew murdered at Munich.

Art Restorer[]

Gabriel, using the alias Mario Delvecchio, worked in Europe as an art restorer. It was itself an art in which he was well-skilled and well-regarded. In Venice, Gabriel worker for Francesco Tiepolo. In England, he worked for Julian Isherwood. Even after returning to work for the Office, Gabriel supported himself between assignments as a restorer. He sometimes excused himself from a restoration project when duty called.

Relationships[]

Leah Allon[]

Leah was Gabriel's first wife. They met at the Betsal'el School of Art in Jerusalem. Together they had a son, Dani, who died when their car was blown up in Vienna. Leah survived but suffered from post-traumatic stress syndrome and psychotic depression leaving her mind practically destroyed. When she began gaining periods during which she was more lucid and aware of her surroundings, she gave Gabriel permission to divorce her and marry Chiara.

Chiara Allon[]

Key Events[]

The Kill Artist[]

Although Gabriel had left the Office, Ari Shamron knew that he was the one to track down and kill Tariq al-Hourani, who had murdered the Israeli ambassador to France and whose brother had been killed by Gabriel for being a terrorist. Gabriel recruited photographer's model Jacqueline Delacroix, who had worked with him during Wrath of God to sleep with al-Hourani's accomplice, Yusef al-Tawfiki. Jacqueline was discovered and taken to Paris, Montreal and New York as a cover for al-Hourani. Gabriel followed as al-Hourani, who had a brain tumor, prepared to murder Yasir Arafat. Gabriel trapped al-Hourani in a stairwell, but al-Hourani shot him. Jacqueline, who had managed to escape from her captivity, shot and killed al-Hourani. Jacqueline's relationship with the Office was revealed and she moved to Israel. Gabriel returned to Israel and saw Yusef al-Tawfiki on a street. Al-Tawfiki admitted having been a double agent working for Shamron.

The English Assassin[]

Allon seeks the murderer of a Zurich banker and Nazi art collector, with banker's violinist daughter in Lisbon, Vienna, London, avoiding the Assassin.

The Confessor[]

Gabriel was restoring a Bellini altarpiece in Venice when he was interrupted by the murder of Benjamin Stern. Ari Shamron sent Gabriel to Munich to learn what he can about Stern's murder and remove any evidence there might be of Stern's former affiliation with the Office. Gabriel went to Munich and met with Detective Axel Weiss of the Munich police and with Helmut Berger, Stern's department head at a university. Gabriel visited the Convent of the Sacred Heart on Lake Garda and spoke with Mother Vincenza, who told him lies about how Jews were treated during World War II. Based on information he acquired near the convent, Gabriel exchanged information about Benjamin Stern and the operations of the Office with Peter Malone shortly before Malone was murdered and his computer stolen by Eric Lange.

A Death in Vienna[]

Allon pursues the Nazi who implemented the furnaces across the globe into Argentina, vindicating the death of a agency friend in Vienna. With Chiara.

Prince of Fire[]

Jeremy Crabbe recognized Gabriel as "Mario Delvecchio" and asked Julian about Gabriel's presence at an auction. Julian explained that Gabriel cleaned paintings for him. Julian purchased Daniel in the Lions' Den based on Gabriel's assurance that is was actually the work of Rubens; Gabriel agreed to clean the painting for £200,000. He then went to visit Leah to tell her about Chiara Zolli, but Leah was unresponsive. Gabriel flew to Venice where he found Ari Shamron waiting for him.Allon follows a terrorist archaeologist through Marseilles then Paris into a trap at the Paris train station, with Leah as bait. He then breaks up with Chiara.

The Messenger[]

The Office acquired a laptop computer that contained photographs indicating a planned attack on the Vatican; the information was analyzed too late to prevent the attack, which badly damaged St. Peter's Basilica and almost killed Pope Paul VII. Returning to Jerusalem, Gabriel learned from Ari Shamron that Ahmed bin Shafiq, who financed the attack, was linked to the Saudis. Shortly thereafter, Shamron was nearly killed by a bomb. Gabriel discussed Ahmed bin Shafiq with Adrian Carter. Carter identified Abdul Aziz al-Bakari as a banker for the Saudi royal family. At the request of President of the United States, Gabriel prepared an operation to kill Ahmed bin Shafiq over Ari Shamron's objections. Gabriel used his relationship with Julian Isherwood to borrow an uncatalogued painting by Vincent van Gogh. Gabriel and Adrian Carter worked together to recruit Sarah Bancroft, an art curator, to penetrate Abdul Aziz al-Bakari's organization to locate Ahmed bin Shafiq. Sarah helped sell the painting to Abdul Aziz al-Bakari, who then, as Gabriel hoped hired Sarah after taking her to a yacht. Sarah's position with the Office was discovered and al-Bakari moved Sarah to Switzerland for interrogation. Office agents caught up, killed the men holding her and freed her. Gabriel returned to the Vatican during a meeting between Pope Paul VII and the President of the United States. A Swiss Guardsman who had converted to Islam attempted to kill the Pope, but failed. Gabriel and unidentified agents broke into al-Bakari's Paris Mansion and recovered the Van Gogh painting which Gabriel then returned to the owner. Gabriel and Eli killed Bin Shafiq with a bomb in Cairo. Gabriel and other agents killed Abdul Aziz al-Bakari; his daughter screamed for revenge.

The Secret Servant[]

Allon pursues the killer of an Office agent, uncovering an arab terrorist ring who kidnaps the US ambassador's daughter. Chiara and Gabriel wed.

Moscow Rules[]

Gabriel and his new wife Chiara were spending their honeymoon at a peaceful farm somewhere in Umbria. Gabriel had returned to his artistic pursuits, and under the disguise of an Italian name he was working like a restorer again. He was working on a Poussin painting for the Pope when he heard that Uzi Navot was in the town wanting to tell him something important. He met Uzi at a restaurant and Navot told him about the death of a Russian journalist and that the journalist's editor wanted to meet Gabriel. Before they could speak, the editor was also killed and Gabriel had to travel to Moscow to find out the truth behind this situation. The main villain is Ivan Kharkov, an oligarch. At the end Gabriel found out the transaction Ivan and his men wanted to manage.

The Defector[]

Allon and Chiara are captured by Ivan Kharkov, who used the Defector Grigori from last book, trying to get his kids back. Moscow, Paris, London, So France.

The Rembrandt Affair[]

A stolen Rembrandt leads to a trail of Nazi stolen money, and to a billionaire Swiss merchant supplying nuclear plants to Iran.

Portrait of a Spy[]

Allon breaks a jihadi network by setting a sting around an art sale, with the daughter of a prior target of GA. Set in Dubai, Saudi desert, London.

The Fallen Angel[]

The English Girl[]

The Heist[]

The English Spy[]

The Black Widow[]

House of Spies[]

The Other Woman[]

The New Girl[]

The Order[]

The Cellist[]

Kills[]

Characters Killed

Aliases[]

Trivia[]

  • Allon, a name selected by Gabriel's mother after she arrived in Israel, means "oak tree."[2]
  • Gabriel lived at 16 Narkiss Street, Jerusalem; the home was a former safe house that an "unidentified friend of the Office" purchased and deeded to Gabriel.[3]
  • Shamron called Gabriel his "Prince of Fire."[4]
  • Gabriel's father taught him that men who could not keep secrets were weak and inferior.[5]
  • Gabriel was twenty-one when Ari Shamron recruited him for operation Wrath of God. This places his birth in 1951 or possibly late 1950[6]
  • His favorite music is "La Bohème" by Puccini
  • Gabriel has a passionate hatred of farming.
  • His first language was German and since he inherited his mother's Berlin accent, he sounds like a native speaker, who had been raised in the center of Berlin.
  • He is fluent in Italian.
  • He has an aversion of tobacco that is known in intelligence all around the world.
  • According to him, Uzi Navot and Eli Lavon are like brothers to him
  • He's not very talkative
  • He carries a picklock around and knows how to use it

References[]

Sours: https://gabrielallon.fandom.com/wiki/Gabriel_Allon

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  • makes you feel unwell but you're ok to carry on as normal - for example, go to work

flu

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  • appears quickly within a few hours
  • affects more than just your nose and throat
  • makes you feel exhausted and too unwell to carry on as normal

cold symptoms can include:

  • blocked or runny nose
  • sore throat
  • headaches
  • muscle aches
  • coughs
  • sneezing
  • a raised temperature
  • pressure in your ears and face
  • loss of taste and smell

the symptoms are the same in adults and children. sometimes, symptoms last longer in children.

causes of colds

colds are caused by viruses. they can easily spread to other people. you're infectious until all your symptoms have gone. this usually takes about a week.

colds are spread by germs from coughs and sneezes which can live on hands and surfaces for 24 hours.

to reduce the risk of spreading a cold you should:

  • wash your hands often with warm water and soap
  • cough into your elbow to stop germs getting on to your hands and spreading to other people
  • use tissues to trap germs when you cough or sneeze
  • bin used tissues as quickly as possible

how to prevent catching a cold

the best ways to avoid catching a cold are:

  • washing your hands with warm water and soap, especially before eating
  • not sharing towels or household items, like cups, with someone who has a cold
  • not touching your eyes or nose. you can infect your body if you've come into contact with the virus.
  • staying fit and healthy
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